Exactly same COG-645 pdf download as real exam, Just memorize and pass

killexams.com gives substantial and exceptional Killexams COG-645 real questions with Actual Test Questions for new goals of IBM COG-645 Exam. Practice these COG-645 dumps questions to Improve your insight and finish your test with High Marks. We 100 percent ensure your accomplishment in the genuine COG-645 test or your cashback. These are not simply COG-645 dumps questions, these are genuine COG-645 questions.

Exam Code: COG-645 Practice exam 2022 by Killexams.com team
IBM Cognos 10 BI Multidimensional Author
IBM Multidimensional Study Guide
Killexams : IBM Multidimensional Study Guide - BingNews https://killexams.com/pass4sure/exam-detail/COG-645 Search results Killexams : IBM Multidimensional Study Guide - BingNews https://killexams.com/pass4sure/exam-detail/COG-645 https://killexams.com/exam_list/IBM Killexams : Barriers and Facilitators for the Implementation of Geriatric Oncology Principles in Mexico: A Mixed-Methods Study

Population aging represents a critical issue for global cancer care. In 2015, approximately 8.5% of worldwide population was age ≥ 65 years.1 This number is expected to increase, mostly in low- and middle-income countries (LMIC).2 Latin America is a widely heterogeneous region largely composed of LMIC, including Mexico. In 2010, approximately 7% of Mexicans were ≥ 65 years, with a projected 116% increase by 2030.3

CONTEXT

  • Key Objective

  • What barriers exist for the implementation of geriatric oncology care in Mexico?

  • Knowledge Generated

  • In a nationwide survey, 18.9% of the Mexican oncology specialists reported routinely performing a geriatric assessment (GA) when treating older adults with cancer; medical oncologists and physicians with a lower patient volume were more likely to use a GA. The most frequent barriers for the routine use of the GA were lack of qualified personnel, limited knowledge, and insufficient time to perform an assessment.

  • Relevance

  • Reported barriers for the implementation of the GA into routine care in oncology in Mexico can potentially be overcome by educational interventions aimed at both oncology and geriatrics specialists. Next steps should focus on improving knowledge and training existing personnel through educational initiatives in cooperation with local societies.

Since most cancer cases are diagnosed in older adults (30%-63%),4,5 providing high-quality care for this population should be a global priority. Therefore, the ASCO6 and the International Society for Geriatric Oncology (SIOG),7 among other international associations, recommend performing a geriatric assessment (GA) in all older adults with cancer as standard of care. The GA is a multidimensional evaluation that can identify impairments in function, comorbidities, falls, psychological status, cognition, and/or nutrition, which are not routinely detected during usual oncology consultations and which are associated with adverse outcomes. Information obtained through a GA can Boost communication with patients and caregivers8 and mitigate treatment-associated toxicity.9,10

Despite mounting evidence favoring its use, GA routine uptake is limited. In the United States, awareness of geriatric oncology guidelines is not widespread; some domains such as functional status and falls are more frequently evaluated, whereas other parameters such as mood and non–cancer-specific mortality risk are seldom assessed.11,12

LMIC have a reduced capacity to provide high-quality geriatric oncology care partly because of lack of personnel and infrastructure,13 but other barriers have not been extensively studied and may differ regionally. To address this, we aimed to describe the status of geriatric oncology knowledge and practice among Mexican oncology professionals and to identify barriers and facilitators for the implementation of GA into routine care of older adults with cancer in Mexico.

This was an explanatory sequential mixed-methods study, involving collecting quantitative data first and then explaining those results with in-depth qualitative data (Fig 1).

Quantitative Data Collection

We administered a web-based survey to oncology specialists in Mexico (medical, radiation, surgical, and gynecologic oncologists), including questions about demographics, awareness of geriatric oncology principles, and the use of the GA and other geriatric oncology tools in everyday practice. Survey questions were selected through literature review11,12 and investigator consensus.

The survey was emailed to 1,240 members of the Mexican Society of Oncology (SMeO) between July and October 2020. Weekly reminders were sent, and respondents were provided incentives through a lottery. The survey was completed and managed through REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ).14

Descriptive statistical analysis of survey data was performed using SPSS 21.0 (IBM Corp, Armonk, NY). Since the primary interest was to estimate the proportion of providers performing a GA, we undertook group comparisons of respondent characteristics for the following question: “For patients ≥ 65 years, do you perform a multidimensional geriatric assessment using validated tools?

First Point of Integration

To select candidates for the qualitative phase, we used maximal variation sampling15 according to the answers to the question on performing a GA. We also selected participants according to the characteristics significantly associated with the use of GA. Interview candidates were invited via email.

Qualitative Data Collection

We developed a semistructured interview guide that was refined after analyzing survey data and piloted with medical oncology fellows. The final guide contained questions about usual care and physicians' decision-making process for older adults with cancer, challenges faced when caring for older patients, referral pathways for geriatric consultations, available personnel and infrastructure, reasons for performing/not performing a GA, ideal workflow for performing a GA in clinical practice, barriers and facilitators for this ideal workflow, and the process for acquiring geriatric oncology knowledge (Data Supplement). The interviewer could ask additional questions. The primary investigator (H.C.V.-A., a female medical oncologist with geriatric oncology research experience) performed semistructured online interviews via Zoom. We planned to interview at least 10 people who reported performing a GA and 10 who did not. Participants continued to be invited, and interviews conducted until thematic saturation was achieved. Consent was verbally obtained before starting each interview. Interviews were recorded, anonymized, and transcribed verbatim. Two investigators (H.C.V.-A. and L.M.B.G.) developed a codebook through open coding, and data were analyzed inductively using thematic analysis. To facilitate integration, themes were developed and elaborated on the basis of survey questions focused on barriers to GA implementation. Coding consistency was discussed regularly, with a third investigator consulted for discrepancies (E.-S.-P.-C.). Themes were refined after discussion with other research team members.

Second Point of Integration

To explain survey results, we developed joint display tables16 to present the identified barriers according to the use of GA and the identified facilitators according to the reported barriers. Qualitative and mixed-methods analyses were performed using MAXQDA 2020 (VERBI Software, Berlin, Germany). This study was approved by INCMNSZ's institutional review board (GER-3358-2020-1).

Survey

The survey was emailed to 1,240 physicians. We obtained 196 valid survey responses (response rate 15.8%). Sixty-one percent of the respondents were male. Ninety-eight participants (50.0%) were surgical oncologists, 59 (30.1%) were medical oncologists, and 38 (19.4%) were radiation oncologists. The median respondent age was 42 years, and the median time in practice was 8 years. Forty percent worked in Mexico City; 34.2% had their primary practice at a public institution, 26.5% at a private institution, and 37.8% at both (Table 1). One hundred twenty-one respondents (61.7%) reported having a geriatrician available at their primary practice site, and 72 (36.7%) reported not having a geriatrician at their primary practice site but having one available for referrals.

Table

TABLE 1 Survey Respondent (N = 196) and Interview Participant Characteristics (n = 22)

Respondents saw patients a median of 5 days per week, with a median of 11-15 patients per day. The median proportion of patients age 65-79 years seen by respondents on a usual clinic day was 30%, with 10% of the patients age ≥ 80 years. Regarding the routine evaluation of GA domains, most reported assessing comorbidity and daily functioning most of the time/always. However, most respondents reported evaluating cognition, depression, and falls less commonly (Table 2).

Table

TABLE 2 Frequency of Evaluation of Geriatric Assessment Domains by Survey Respondents (N = 196)

Thirty-seven physicians (18.9%) reported performing a multidimensional GA using validated tools when treating older patients with cancer. Male respondents (P = .026), medical oncologists (P = .002), and those seeing ≤ 10 patients per day (P = .010) were more likely to report performing a GA (Table 3). Physicians who reported performing a GA were also younger that those who did not (median age 37 v 43 years, P = .032).

Table

TABLE 3 Characteristics Associated With the Reported Use of a GA When Treating Older Patients With Cancer

Most respondents reported using performance status (96.4%), comorbidities (93.4%), life expectancy (81.1%), age (74.5%), and patient preferences (70.4%) as parameters to guide treatment decision making. On the contrary, only 29.1% and 22.4% reported using toxicity calculators and GA as guidance for decision making.

Regarding barriers for GA use in clinical practice, 37.2% reported lack of time, 49.0% lack of qualified personnel, 43.9% lack of knowledge on how to use GA tools, 8.7% a lack of impact of information provided by GA in their practice, 8.2% prohibitive cost, and 5.6% patient unwillingness to undergo GA. Lack of knowledge as a barrier was reported by a larger proportion of respondents who reported not routinely performing GA (51.6 v 10.8%, P < .001). There were no other differences in reported barriers between those who reported performing GA and those who did not. Almost all respondents (95.9%) wanted to receive additional information and training in geriatric oncology.

Semistructured Interviews

We sampled survey respondents according to their use of GA, medical specialty, and size of practice. We also ensured that all interview participants practiced at different institutions. A total of 91 respondents were invited via email to be interviewed. Twenty-six respondents accepted the invitation, and ultimately we performed 22 interviews (Table 1). The most reported barriers to GA use (at least once) were unavailability of geriatricians (n = 15), lack of time (n = 12), system-related barriers (n = 10), lack of interest (n = 9), and the COVID-19 pandemic (n = 9). We related interview answers to our prior survey questions about barriers to deepen our understanding. On Table 4, we present illustrative quotes from physicians who reported routinely performing a GA versus those who did not.

Table

TABLE 4 Barriers to the Implementation of Geriatric Oncology Principles

Lack of Qualified Personnel as a Barrier

Physicians with private practice only may not have regular contact with geriatricians for referrals: “No one has visited me to say ‘hi, I'm a geriatrician and I would like to work with you.’” In some cases, geriatricians were not available in the respondent's city or region. On the other hand, public practice sites with oncology specialists are usually second- or third-level hospitals, where geriatricians are available. However, in these settings, even if human and physical resources exist, access to a geriatrician is usually limited by patient volume: “At IMSS [Mexican Social Security Institution], the service is available, but access is very difficult due to the number of patients.”

For physicians who reported geriatrician availability, an additional problem was the perception that geriatricians have insufficient oncological training, limiting their integration into multidisciplinary cancer care teams: “If a geriatrician is not officially familiar with this area of knowledge, it's more difficult to have a conversation with them.”

Lack of Knowledge as a Barrier

Starting from fellowship, training in geriatric oncology is insufficient. One recently graduated interviewee said: “During fellowship, in my training, we didn't cover a lot of specifics on older patients.” Knowledge deficiencies encompass not only the utilization of screening and assessment tools but also interpretation of information provided by GA: “…because [a tool] provides a number that says ‘the benefit is x,’ but what does that really mean?”, limiting the perceived usefulness of these assessments. Some participants perceive a general lack of interest in geriatric oncology and in the use of the GA, which may derive from limited inclusion of geriatric oncology principles in educational curricula.

Lack of Time as a Barrier

Overcrowding of oncology services is common in public institutions, limiting the inclusion of the GA into routine appointments: “Sometimes I've opened my life expectancy calculator to get a Suemoto index. I don't do it always, it depends on the workload.” Another problem is the lack of effective referral pathways for GA: geriatricians may not be in the same practice site as the oncology physician, causing patients to spend excessive amounts of time or making it impossible to have a GA before starting treatment: “In institutions such as IMSS or ISSSTE, the patient requests an appointment and gets it three or four months later.”

Facilitators

The most common facilitators for GA reported at least once during interviews were availability of geriatricians (n = 19), system and administrative facilitators (n = 14), availability of a multidisciplinary team (n = 11), interest in geriatric oncology (n = 10), and patient factors (n = 10). In Table 5, we present quotes relating barriers and facilitators through a joint display, as well as potential strategies to use those facilitators as solutions.

Table

TABLE 5 Facilitators and Proposed Solutions to Boost the Use of Geriatric Oncology Principles

In this mixed-methods study, 18.9% of the surveyed oncology specialists in Mexico reported using a GA when caring for older adults with cancer. Barriers for the routine implementation of the GA included lack of qualified personnel for performing GA, lack of knowledge on how to perform and interpret a GA, and perceived lack of time for performing these assessments. Availability of geriatricians, presence of a multidisciplinary team, and personal interest in geriatric oncology were common facilitators for those who have successfully implemented the GA in their practice.

The proportion of surveyed physicians who reported performing a GA resembled that reported in other studies, such as a survey of community oncologists in the United States (20%).12 In our study, GA use was higher among some subgroups, such as medical oncologists (33%). Information regarding GA use in routine practice in LMIC, including Latin America, is very limited. A Brazilian survey among medical oncologists showed that awareness of geriatric oncology principles is widespread but insufficiently applied.17 In Mexico, currently there are no specific geriatric oncology guidelines, but it is commonplace to follow recommendations from international societies. However, although performing a GA for older patients with cancer is recommended by ASCO and SIOG, implementation of these recommendations is not widespread.11

In our survey, we found that GA domains which may be perceived as part of a routine oncology evaluation, such as daily functioning and comorbidities, were more commonly evaluated than others. Oncologists may not feel comfortable or qualified enough to perform a cognitive assessment, for example, and some respondents mentioned that they preferred that a geriatrician or a neurologist undertook such assessments. Additionally, even short cognitive screening tools may be difficult to implement in busy practices, resulting in physicians attempting to do simpler screenings, such as only asking about memory and orientation, which may not be sensitive enough to detect patients who could benefit from a thorough cognitive evaluation.

The most common barriers identified in our survey are similar to those found in higher resource settings. For example, the main barriers to GA use in an Australian survey were lack of time, little contact with geriatricians, and low availability of referral services.18 In Spain, the main barriers were lack of time, low availability of geriatricians, and organizational barriers, such as high workloads.19 Another common concern across countries is the lack of focused training on geriatric oncology.20-22

Respondents highlighted the limited availability of geriatric oncology training as an important barrier, both for oncologists and geriatricians. Most oncology specialists in Mexico receive little or no training in geriatrics and consequently are insufficiently prepared to identify important issues that may affect cancer-related outcomes. This was also reflected in our survey through the less common evaluation of some GA domains.

Another barrier for the implementation of geriatric oncology is the lack of geriatricians, with only around 600 board-certified geriatricians in the country,23 most of them in larger cities. It is remarkable that whereas some respondents mentioned that geriatrics residents or medical research fellows could perform the GA in patients with cancer, no participants identified nurses or other allied health professionals for this role, as it happens in other countries.24,25 Physician assistants or nurse practitioners are not available in Mexico, and registered nurses specialized in oncology mainly focus on the management, administration, and follow-up of systemic and radiation therapy, thus limiting the participation of nursing professionals in clinical care. The lack of on-site personnel trained at performing GA can hamper interdisciplinary communication, timely referrals, and the development of multidisciplinary teams. Our group previously reported on our local geriatric oncology clinic experience, which works under a consultative model.26 However, this model may not be adequate across all settings since the Mexican health care system is highly fragmented, and available personnel, workload, and resources differ greatly among public and private providers, and even between hospitals within the same public system.27

This is the first study to report on barriers to the use of geriatric oncology principles in Latin America. Its strengths include the participation of multiple specialties in our survey and interviews, providing a wide view of current practices. A limitation for the generalizability of our results is the relatively low response rate of 15.8%. However, this is comparable with other similar surveys.19 It is also likely that our results were prone to a selection bias, with physicians more interested in geriatric oncology more likely to respond. Interestingly, this study has helped us reach potential champions for developing geriatric oncology initiatives in the country. Our survey showed that younger physicians were more likely to incorporate GA in their practice, and some interview participants mentioned that younger specialists, residents, and fellows are interested in the field of geriatric oncology, which also seems to be the case in other parts of the world. For example, a survey of Canadian radiation oncology residents found most of them agreed on the importance of integrating geriatric oncology training into their curriculum.28

Using the information obtained from this study, we propose the following initial actions to Boost the implementation of geriatric oncology principles in Mexico:

  1. To mitigate the lack of qualified personnel, trainees in all areas of health care (residents, fellows, research interns, and paramedic personnel) need to receive training in geriatric oncology. Bidirectional training between oncologists and geriatricians can allow each specialist to Boost interdisciplinary communication. Ideally, this should be incorporated into medical/nursing school, residency, and fellowship training programs, as recommended by global oncology curricula.29

  2. To Boost knowledge, available educational infrastructure (such as that of universities or local oncology societies such as SMeO) could be used to provide online training and continuous medical education in geriatric oncology. Short, focused training programs should emphasize the benefits of GA and GA-driven interventions on hard oncologic outcomes, such as decreasing toxicity and facilitating treatment completion,9,30,31 and include practical workshops on the use of GA tools. Fortunately, openness to such programs is high, with > 95% of the respondents expressing an interest in acquiring more geriatric oncology knowledge.

  3. Lack of time might be the most complicated barrier to address because of the heterogeneity of the Mexican health care system. Using geriatric screening tools such as the G8 could help select patients who benefit the most from full GA and decrease service saturation.32 When possible, geriatric screening and other GA tools should be integrated to the electronic medical record. Using idle times during visits to perform GA might increase patient and physician acceptability. To achieve this, shared care models with integration of the geriatrician on the site (geriatric oncology-focused clinics) might be appropriate.

In conclusion, the main barriers for the implementation of geriatric oncology principles into routine practice in Mexico include the lack of qualified personnel to administer a GA and a lack of knowledge. These barriers can potentially be overcome by educational interventions aimed at both oncology and geriatrics specialists. Our next steps will focus on improving knowledge and training existing personnel through educational initiatives in cooperation with SMeO. In subsequent years, the creation of national working groups and guidelines integrating geriatric oncology principles could help integrate GA into routine oncology practice. Increasing knowledge and interest in geriatric oncology could pave the way to facilitate communication with national key stakeholders for larger initiatives aimed at providing high-quality patient-centered care for all older adults with cancer in Mexico, and those initiatives could be easily transferred to other countries across the Latin American region.

© 2022 by American Society of Clinical Oncology
DISCLAIMER

Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the American Society of Clinical Oncology or Conquer Cancer.

PRIOR PRESENTATION

Presented at the 2021 American Society of Clinical Oncology (ASCO) Annual Meeting (e-abstract), virtual, May 28, 2021; the 2021 Mexican Society of Oncology (SMeO) National Oncology Meeting (poster)Monterrey, Mexico, October 27, 2021; and the 2021 International Society of Geriatric Oncology (SIOG) Annual Conference (oral abstract), virtual, November 4, 2021.

SUPPORT

Supported by a Conquer Cancer Foundation 2020 Global Oncology Young Investigator Award (Dr H.C.V.-A.; Project ID: 16517).

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are selfheld unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/go/authors/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Haydee C. Verduzco-Aguirre

Travel, Accommodations, Expenses: Bristol Myers Squibb (Mexico)

Hector Martínez-Said

Consulting or Advisory Role: MSD Oncology, Novartis

Speakers' Bureau: MSD Oncology, Novartis

Gregorio Quintero Beulo

Employment: Bristol Myers Squibb

Stock and Other Ownership Interests: Bristol Myers Squibb

Honoraria: AstraZeneca

Speakers' Bureau: AstraZeneca

Supriya G. Mohile

Consulting or Advisory Role: Seattle Genetics

Research Funding: Carevive (Inst)

Enrique Soto-Perez-De-Celis

This author is a member of the JCO Global Oncology Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.

Research Funding: Roche

No other potential conflicts of interest were reported.

1. GBD 2017 Mortality Collaborators: Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 392:1684-1735, 2018 Crossref, MedlineGoogle Scholar
2. He W, Goodkind D, Kowal P: An Aging World: 2015, International Population Reports, P95/16-1. Washington, DC, US Census Bureau, 2016 Google Scholar
3. Aggarwal A, Unger-Saldaña K, Lewison G, et al: The challenge of cancer in middle-income countries with an ageing population: Mexico as a case study. Ecancermedicalscience 9:536, 2015 Crossref, MedlineGoogle Scholar
4. Bray F, Ferlay J, Soerjomataram I, et al: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394-424, 2018 Crossref, MedlineGoogle Scholar
5. Pilleron S, Soerjomataram I, Soto-Perez-de-Celis E, et al: Aging and the cancer burden in Latin America and the Caribbean: Time to act. J Geriatr Oncol 10:799-804, 2019 Crossref, MedlineGoogle Scholar
6. Mohile SG, Dale W, Somerfield MR, et al: Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol 36:2326-2347, 2018 LinkGoogle Scholar
7. Wildiers H, Heeren P, Puts M, et al: International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol 32:2595-2603, 2014 LinkGoogle Scholar
8. Mohile SG, Epstein RM, Hurria A, et al: Communication with older patients with cancer using geriatric assessment: A cluster-randomized clinical trial from the National Cancer Institute Community Oncology Research Program. JAMA Oncol 6:196-204, 2020 Crossref, MedlineGoogle Scholar
9. Li D, Sun CL, Kim H, et al: Geriatric Assessment-Driven Intervention (GAIN) on chemotherapy-related toxic effects in older adults with cancer: A randomized clinical trial. JAMA Oncol 7:e214158, 2021 Crossref, MedlineGoogle Scholar
10. Mohile SG, Mohamed MR, Xu H, et al: Evaluation of geriatric assessment and management on the toxic effects of cancer treatment (GAP70+): A cluster-randomised study. Lancet 398:1894-1904, 2021 Crossref, MedlineGoogle Scholar
11. Dale W, Williams GR, R MacKenzie A, et al: How is geriatric assessment used in clinical practice for older adults with cancer? A survey of cancer providers by the American Society of Clinical Oncology. JCO Oncol Pract 17:336-344, 2021 LinkGoogle Scholar
12. Mohile SG, Magnuson A, Pandya C, et al: Community oncologists' decision-making for treatment of older patients with cancer. J Natl Compr Canc Netw 16:301-309, 2018 Crossref, MedlineGoogle Scholar
13. Kanesvaran R, Mohile S, Soto-Perez-de-Celis E, et al: The globalization of geriatric oncology: From data to practice. Am Soc Clin Oncol Ed Book 40:1-9, 2020 MedlineGoogle Scholar
14. Harris PA, Taylor R, Minor BL, et al: The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 95:103208, 2019 Crossref, MedlineGoogle Scholar
15. Palinkas LA, Horwitz SM, Green CA, et al: Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm Policy Ment Health 42:533-544, 2015 Crossref, MedlineGoogle Scholar
16. Guetterman TC, Fetters MD, Creswell JW: Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Ann Fam Med 13:554-561, 2015 Crossref, MedlineGoogle Scholar
17. Pontes LDB, Bugano Diniz Gomes D, Maia Loureiro LV, et al: Physicians expertise in geriatric oncology care: A web survey among Brazilian medical oncologists. J Clin Oncol 32, 2014 (suppl; absrt e20527) LinkGoogle Scholar
18. To THM, Soo WK, Lane H, et al: Utilisation of geriatric assessment in oncology—A survey of Australian medical oncologists. J Geriatr Oncol 10:216-221, 2019 Crossref, MedlineGoogle Scholar
19. Gironés R, Morilla I, Guillen-Ponce C, et al: Geriatric oncology in Spain: Survey results and analysis of the current situation. Clin Transl Oncol 20:1087-1092, 2018 Crossref, MedlineGoogle Scholar
20. Jonker JM, Smorenburg CH, Schiphorst AH, et al: Geriatric oncology in the Netherlands: A survey of medical oncology specialists and oncology nursing specialists. Eur J Cancer Care (Engl) 23:803-810, 2014 Crossref, MedlineGoogle Scholar
21. Kalsi T, Payne S, Brodie H, et al: Are the UK oncology trainees adequately informed about the needs of older people with cancer? Br J Cancer 108:1936-1941, 2013 Crossref, MedlineGoogle Scholar
22. Maggiore RJ, Gorawara-Bhat R, Levine SK, et al: Perceptions, attitudes, and experiences of hematology/oncology fellows toward incorporating geriatrics in their training. J Geriatr Oncol 5:106-115, 2014 Crossref, MedlineGoogle Scholar
23. Gomez-Moreno C, Verduzco-Aguirre H, Soto-Perez-de-Celis E: Geriatric oncology in Mexico. Ecancermedicalscience 14:1102, 2020 Crossref, MedlineGoogle Scholar
24. Overcash J, Ford N, Erdeljac HP, et al: Comprehensive geriatric assessment in oncology: Best practices in caring for older patients. J Adv Pract Oncol 9:640-644, 2018 MedlineGoogle Scholar
25. Burhenn PS, McCarthy AL, Begue A, et al: Geriatric assessment in daily oncology practice for nurses and allied health care professionals: Opinion paper of the Nursing and Allied Health Interest Group of the International Society of Geriatric Oncology (SIOG). J Geriatr Oncol 7:315-324, 2016 Crossref, MedlineGoogle Scholar
26. Verduzco-Aguirre HC, Navarrete-Reyes AP, Chavarri-Guerra Y, et al: The effect of a geriatric oncology clinic on treatment decisions in Mexican older adults with cancer. J Am Geriatr Soc 67:992-997, 2019 Crossref, MedlineGoogle Scholar
27. Strasser-Weippl K, Chavarri-Guerra Y, Villarreal-Garza C, et al: Progress and remaining challenges for cancer control in Latin America and the Caribbean. Lancet Oncol 16:1405-1438, 2015 Crossref, MedlineGoogle Scholar
28. Leifer R, Bristow B, Puts M, et al: National survey among radiation oncology residents related to their needs in geriatric oncology. J Cancer Educ 34:9-13, 2019 Crossref, MedlineGoogle Scholar
29. Dittrich C, Kosty M, Jezdic S, et al: ESMO/ASCO recommendations for a global curriculum in medical oncology edition 2016. ESMO Open 1:e000097, 2016 Crossref, MedlineGoogle Scholar
30. Mohile SG, Mohamed MR, Culakova E, et al: A geriatric assessment (GA) intervention to reduce treatment toxicity in older patients with advanced cancer: A University of Rochester Cancer Center NCI Community Oncology Research Program Cluster Randomized Clinical Trial (CRCT). J Clin Oncol 38, 2020 (suppl; absrt 12009) Google Scholar
31. Lund CM, Vistisen KK, Olsen AP, et al: The effect of geriatric intervention in frail older patients receiving chemotherapy for colorectal cancer: A randomised trial (GERICO). Br J Cancer 124:1949-1958, 2021 Crossref, MedlineGoogle Scholar
32. McKenzie GAG, Bullock AF, Greenley SL, et al: Implementation of geriatric assessment in oncology settings: A systematic realist review. J Geriatr Oncol 12:22-33, 2021 Crossref, MedlineGoogle Scholar
Mon, 11 Apr 2022 05:40:00 -0500 en text/html https://ascopubs.org/doi/full/10.1200/GO.21.00390
Killexams : Global Clinical Trial Management System Market Is Expected To Reach USD 4.45 Billion Registering A CAGR Of 12% And Forecast To 2029

Clinical Trial Management System Market Is Expected To Reach USD 4.45 Billion By 2029 At A CAGR Of 12 percent.

A report on the Global Clinical Trial Management System Market by Maximize Market Research offers a thorough analysis for the forecast period of 2022 to 2029.

Clinical Trial Management System Market Scope:

The report provides in-depth market information for industry participants, covering previous, ongoing, and future changes in the sector as well as projected market size and trends. Additionally, it offers a concise description of challenging market data. Every industry sector is analysed, with a focus on significant businesses such as market leaders, underperformers, and recent newcomers. The complete PESTLE analysis for each country is included in the report. The report may be used as an investor’s guide because it provides a complete analysis of the competitive environment among the top players in the Clinical Trial Management System market by goods and services, revenue, financial state, portfolio, growth plans, and geographic presence.

Request For Free sample @ https://www.maximizemarketresearch.com/request-sample/9837 

Clinical Trial Management System Market Overview:

The crucial set of tools for efficiently planning, managing, and tracking your clinical research portfolio is the clinical trial management system. The study team is guided by this customised, all-inclusive project management tool from study launch through enrolment and monitoring to study close

Clinical Trial Management System Market Dynamics:

The Clinical Trials Management System (CTMS) market was greatly affected by the COVID-19 pandemic. This included problems with clinical trials, difficulties finding patients, and postponed or cancelled investigations. However, a number of strategic actions taken by governments, regulatory organisations, and market stakeholders to guarantee R&D continuity gradually lessened the adverse impact. Decentralized clinical trials were more widely used as a result of the requirement to create a vaccine against the coronavirus.

To immediately advance COVID-19 vaccine equity, the International Federation of Pharmaceutical Manufacturers & Associations (IFPMA) published guidelines. The IFPMA announced that 14 additional members were in the clinical development stage, and that 5 of its members had gotten permission for COVID-19 vaccines. Additionally, as of February 2022, more than 750 research were registered for COVID-19 vaccine development under COVID-NMA, a WHO project. As a result, CTM system use is anticipated to rise.

The pandemic caused operational gaps in the majority of ongoing clinical studies and delayed subject enrolment, according to a research published in the U.S. National Library of Medicine in 2020. Consequently, trial programmes and data integrity were significantly harmed. The majority of sites conducting clinical trials worldwide, other from COVID-19, were discovered to have timeliness issues. Clinical trials have been discovered to completely stop operating in some instances, which has an effect on the results of clinical research. The U.S. FDA released guidelines on how to conduct clinical trials of medical products during the pandemic in March 2020. Similar recommendations assisted in the restoration of clinical trial activities, and it is anticipated that this number will grow over time.

Medical research efforts are being promoted by increased government financing as well as investments made by biotechnology and pharmaceutical companies. It is projected that this factor, together with technical improvements, will accelerate market expansion throughout the projection period. For instance, cloud-based CTMS removes the costs of purchasing, installing, deploying, maintaining, providing support, and licencing software. These solutions update software and patch management programmes automatically, easing the workload of internal IT workers and saving money. Additionally, the cloud-based software offers mobile access to the server with the highest level of data protection.

During the forecast period, market growth rate is expected to be boosted by the rising number of decentralised clinical studies. These also go by the names virtual, digital, mobile, site-less, and remote trails, and they frequently make use of telemedicine services.

Clinical Trial Management System Market Regional Insights:

The Asia Pacific region is expected to hold the major share of the Clinical Trial Management System market over the forecast period. The Asia Pacific region is expected to hold the major volume of the Clinical Trial Management System market over the forecast period. This is because there are more R&D activities taking place in the area, there is a huge patient pool available, more clinical trials are being undertaken there, and clinical trials are being outsourced. Asian nations provide a less expensive method for doing clinical research investigations. During the projection period, it is anticipated that this aspect will strengthen the local market. Another developing market for clinical trials is Latin America. According to clinical trials.gov, more clinical trials are being carried out in Latin American nations like Mexico, Brazil, and Argentina.

Clinical Trial Management System Market Segmentation:

By Type:

By Component:

By Delivery Mode:

By End-User:

Clinical Trial Management System Market Key Competitors:

To Get A Copy Of The sample of the Clinical Trial Management System Market, Click Here @ https://www.maximizemarketresearch.com/market-report/global-clinical-trial-management-systems-market/9837/ 

About Maximize Market Research:

Maximize Market Research is a multidimensional market research and consulting firm that hires experts from several industries. Our coverage includes businesses that manufacture medical devices, pharmaceutical companies, science and engineering, electronic components, industrial equipment, technology and communication, automobiles, chemical goods and substances, general merchandise, alcoholic beverages, personal care products, and automated systems. We offer a range of services, including client impact studies, competitive analysis, production and demand analysis, market-verified industry estimations, and technology trend analysis, to name a few.

Contact Maximize Market Research:

3rd Floor, Navale IT Park, Phase 2

Pune Banglore Highway, Narhe,

Pune, Maharashtra 411041, India

[email protected]

Sun, 10 Jul 2022 19:22:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/global-clinical-trial-management-system-market-is-expected-to-reach-usd-4-45-billion-registering-a-cagr-of-12-and-forecast-to-2029 Killexams : Global Oncology Information System Market Value Projected To Reach USD 16.7 Billion At A CAGR Of 9.41% And Forecast To 2029

Oncology Information System Market Is Expected To Reach USD 16.7 Billion By 2029 At A CAGR Of 9.41 percent.

Maximize Market Research report on the Global Oncology Information System Market provides a complete analysis for the forecast period of 2022 to 2029.

Oncology Information System Market Scope:

For industry participants, the report offers in-depth market insights that cover information on the most recent, ongoing, and anticipated changes in the sector as well as forecasts for market size and trends. Additionally, it offers a succinct explanation of challenging market data. Every industry sector is analysed, with a focus on significant businesses such as market leaders, underperformers, and recent newcomers. The complete PESTLE analysis for each country is included in the report. Because it offers a thorough analysis of the competitive landscape of the major rivals in the Oncology Information System market by goods and services, revenue, financial state, portfolio, growth strategies, and geographical presence, the study works as an investor’s guide.

Request For Free sample @ https://www.maximizemarketresearch.com/request-sample/6591 

Oncology Information System Market Overview:

The oncology information system (OIS) is a complete information and image management solution that makes it easier to manage and optimise cancer patients’ profiles and care. The oncology information system keeps track of patient data including diagnoses, medical history, treatment regimens, and medications. The purpose of this system is to forecast treatment outcomes, organise the patient’s care accordingly, and share data on cancer patients with other healthcare organisations. Oncology information systems (ois) are crucial instruments for enhancing patient safety, aiding research, and gauging the success of practise guidelines.

Oncology Information System Market Dynamics:

The main factor driving the growth of the global oncology information system market is the rising incidence of cancer cases and the diversity of treatment modalities, which have compelled healthcare providers to adopt an extensive data management system for maintaining large data sets, including patient records and treatment regimens. The electronic health record (EHR) and electronic medical record (EMR) maintenance, patient treatment pattern maintenance, and treatment prediction are all goals of these data management systems, often known as OIS. Through a single interface and workflow, these capabilities provide room for automation, accessing, and preserving patient records. Therefore, the global oncology information system market is expected to develop throughout the course of the projected period as a result of the rise in cancer prevalence.

The major restraint on the growth of the global oncology information system market is the dearth of qualified IT workers in the healthcare sector. In hospitals, a significant obstacle to deploying OIS solutions is a shortage of qualified personnel. Currently, the demand for healthcare IT specialists considerably outpaces the supply in major countries like the US and Europe as well as developing ones. The overall load is increased further by the expanding number of healthcare IT efforts.

Oncology Information System Market Regional Insights:

The Asia Pacific region is expected to hold the major share of the Oncology Information System market over the forecast period. The Asia Pacific region is expected to hold the major volume of the Oncology Information System market over the forecast period. This is due to the constantly evolving healthcare infrastructure, growing modernization-related projects, and growing government efforts to increase public access to contemporary healthcare systems. The global market for oncology information systems is expanding as a result of increased cancer incidence rates and rising expenditures for the use of IT solutions in healthcare settings. A bad prognosis and a higher death rate are associated with particular malignancies.

Oncology Information System Market Segmentation:

By Product & Services:

  • Patient Information Systems
  • Treatment Planning Systems
  • Chemotherapy Information System
  • Consulting/Optimization Services
  • Implementation Services
  • Post-Sale and Maintenance Services

By Application:

  • Radiation Oncology
  • Medical Oncology
  • Surgical Oncology

By End-User:

  • Hospitals & Diagnostic Imaging Centers
  • Ablation Care Centers and Cancer Care Centers
  • Government Institutions
  • Research Facilities

Oncology Information System Market Key Competitors:

  • Bizmatics, Inc.
  • Siemens Healthcare
  • IMPAC Medical Systems, Inc.
  • CureMD Corporation
  • Raysearch Laboratories
  • Varian Medical Systems, Inc.
  • Mckesson Corporation
  • Koninklijke Philips N.V.
  • Epic Systems Corporation
  • Elekta AB
  • Cerner Corporation
  • Altos Solutions, Inc.
  • Accuray Incorporated
  • IBM
  • Advanced Data Systems
  • Bogardus Medical Systems, Inc
  • F. Hoffmann-La Roche AG
  • Altai Oncology, LLC.
  • GE Healthcare
  • OPTUM
  • Allscripts Healthcare Solutions, Inc.
  • MICA Information Systems, Inc.
  • EndoSoft LLC
  • MIM Software Inc.

To Get A Copy Of The sample of the Oncology Information System Market, Click Here @ https://www.maximizemarketresearch.com/market-report/global-oncology-information-system-market/6591/ 

About Maximize Market Research:

Experts from a range of industries are employed by the multidimensional market research and consulting firm Maximize Market Research. We concentrate on a wide range of sectors, such as those that make medical devices, pharmaceutical firms, science and engineering, electronic components, industrial equipment, cars, chemical goods and substances, general merchandise, alcoholic beverages, personal care products, and automated systems. Client impact studies, competitive analysis, production and demand analysis, and market-verified industry estimations are a few of the services we provide.

Contact Maximize Market Research:

3rd Floor, Navale IT Park, Phase 2

Pune Banglore Highway, Narhe,

Pune, Maharashtra 411041, India

[email protected]

Sun, 10 Jul 2022 20:55:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/global-oncology-information-system-market-value-projected-to-reach-usd-16-7-billion-at-a-cagr-of-9-41-and-forecast-to-2029
Killexams : Comparison of Annotation Services for Next-Generation Sequencing in a Large-Scale Precision Oncology Program

The framework to implement genomics-driven therapeutic oncology has been rapidly established in leading cancer centers worldwide. An exponential growth in characterized cancer genomes has ensued.1 The results of next-generation sequencing (NGS) assays are intricate and provide a multitude of somatic mutations in hundreds of genes. Mutation rates vary significantly between different cancers.2 Many somatic genetic variants are characterized as variants of unknown significance, and the implication of multiple mutations within a single tumor is poorly defined.3 Moreover, tumors possess intratumoral genetic heterogeneity, adding another layer of complexity to NGS results.4

CONTEXT

  • Key Objective

  • Algorithms to interpret the pathogenicity and clinical actionability of next-generation sequencing (NGS)–detected sequence variants have been implemented; however, little is known about their relative performance in clinical practice. The clinical interpretation of genomic alterations remains a major bottleneck for realizing precision medicine. We sought to examine the concordance for pathogenicity and clinical actionability of three annotation services available to the VA Precision Oncology Program (N-of-One, OncoKB, and Watson for Genomics).

  • Knowledge Generated

  • We demonstrate that NGS annotation services have wide-ranging agreements in pathogenicity (30%-76%). Moreover, there was moderate agreement (96.9%) in level 1 drug actionability.

  • Relevance

  • We anticipate these findings will encourage improvement in the precision of NGS multigene panel annotation. We provide detailed information regarding NGS panels, gene variant pathogenicity, annotation ontology, and level 1 drug actionability by annotation service. This study has significant implications for precision oncology clinical trials and molecular tumor boards.

At many institutions, including the Veterans Affairs (VA) healthcare system, molecular tumor boards (MTBs) have been instituted to interpret the results of NGS and develop treatment recommendations. In some instances the whole genome is sequenced, and in other cases selected subsets of genes are sequenced with smaller panels. Identified alterations that are of particular clinical interest include driver mutations or druggable mutations.1 For use in clinical decision making, identified gene variants must be annotated or interpreted in terms of the likelihood of their tumorigenicity and drug actionability.5 There are many resources to assist with data curation, including OncoKB (Memorial Sloan Kettering Cancer Center, New York, NY),6 My Cancer Genome (Vanderbilt-Ingram Cancer Center, Nashville, TN),7 Precision Medicine Knowledge Base (Weill Cornell Medicine Englander Institute for Precision Medicine, New York, NY),8 Personalized Cancer Therapy (MD Anderson Cancer Center, Houston, TX),9 CIViC (Washington University in St Louis School of Medicine, St Louis, MO),10 Jackson Laboratory Clinical Knowledgebase (Jackson Laboratory, Bar Harbor, ME),11 Cancer Genome Interpreter (Barcelona, Spain),5 Cancer Driver Log (omicX, Le-Petit-Quevilly, France),12 and N-of-One (NoO; Concord, MA).13 The identification of mutations that are pathogenic and actionable is generally performed by multidisciplinary teams who reference genomic databases, published literature, and clinical trials. The development of cognitive computing, such as Watson for Genomics (WfG), has also garnered interest in precision oncology. WfG14 is a cloud-based service that uses computerized models to simulate human thought to analyze large volumes of genome data and generate evidence-based guidelines.

The ultimate objective of gene sequencing is to Boost patient outcomes by matching patients to specific treatments that target mutations driving the growth of individual tumors. The landscape of genetic tests has evolved from single mutation to small hotspot mutation panels, large gene panels, and whole exome and whole genome platforms. The interpretation of the clinical significance of these genomic mutations has become a formidable task because of the number of genes tested and limited understanding of normal genetic variation as well as pathogenic gene-to-gene interaction. Although professional societies have recently published consensus guidelines15 for the use and interpretation of NGS, they have not been extensively used in practice. Moreover, traditional clinical trial design approaches of molecularly unselected tumor types may no longer be appropriate because of the molecular heterogeneity of tumors from each primary site (eg, breast). This has led to the development of basket and umbrella trial designs as well as precision genomic oncology trials at the point of care. An urgency exists to examine the various annotation services and clinical support tools available to ensure quality of NGS in oncology.

The VA National Precision Oncology Program (NPOP)16 initially used two commercial vendors for NGS testing, Personal Genome Diagnostics (PGDx, Baltimore, MD) and Personalis (Menlo Park, CA), that used the NoO commercial annotation service. In addition, WfG was available to the VA through a gift from IBM. Until NPOP accrues substantial outcomes data, treatment recommendations are largely based on biomarker panels, annotations, and relevant patient characteristics.17 NPOP also references open data sources, including OncoKB (an evidence source, not an annotation provider), when interpreting pathogenicity and actionability of gene variants. For some variants and cases, annotation is assisted by a molecular pathologist. The primary objective of this study was to assess the concordance for pathogenicity determination and clinical actionability for the annotation services available to NPOP.

Sequencing data from all patients who underwent NGS testing under NPOP since its genesis in 2015 through 2017 were analyzed. The work described here was conducted to assess the quality of annotation services available to the NPOP, a clinical operational program that is not research and does not require institutional review board review; permission to publish was obtained from the appropriate VA authority. Details of the NPOP have been published previously.16 The goal is to use NGS testing to facilitate patient access to US Food and Drug Administration (FDA)–approved targeted therapies and immune checkpoint inhibitors and increase participation in clinical trials (Data Supplement). Clinical trials have been the recommended option in > 50% of cases for which there was no FDA-approved therapy. NGS results were generated through two contracted vendors: PGDx18 (CancerSELECT 125, PlasmaSELECT 64, CancerSELECT 88, and CancerSELECT 203) and Personalis (ACE Cancer Plus 181)19 (Data Supplement).

We matched gene variants for the two contracted vendors, PGDx and Personalis, to compare vendor recommendations as well as commercial annotation services for the same unique gene variant. NGS-detected variants were annotated by the sequencing vendor using the commercial annotation service NoO. We examined NoO as implemented by the vendors. NPOP staff re-annotated DNA sequence results using WfG and OncoKB. IBM donated use of WfG to the VA; OncoKB is created and maintained by Memorial Sloan Kettering Cancer Center (MSKCC) and available online.6

After completion of sequencing, results were uploaded by an NPOP data scientist to WfG (including tumor type, a list of sequence variants as a variant call file, presence of gene fusions, and gene copy number variation). Results were routinely uploaded into WfG as part of routine test interpretation workflow. Batch analysis was performed approximately weekly using a custom-built informatics workflow and a packaging tool provided by IBM. WfG assigned a pathogenicity label to each variant or fusion. Drug matching for pathogenic variants is also part of the WfG analysis pipeline. Variants that are pathogenic or likely pathogenic are matched with FDA-approved drugs and actively recruiting clinical trials using levels of evidence (level 1, 2A, 2B, 3A, 3B, 4, or R1; Tables 1 and 2).3 For actionability, WfG uses a subset of National Cancer Institute (NCI) thesaurus terms for diagnosis coding.20 WfG was performed on all samples as part of NPOP workflow, and OncoKB analysis was performed on an ad hoc basis in response to NPOP consults and/or MTB cases.

Table

TABLE 1. Levels of Evidence Used by Watson for Genomics

Table

TABLE 2. Levels of Evidence Used by OncoKB

Using the same unique gene variants annotated by NoO and WfG, we queried all variants using the OncoKB curated database for the current study (Data Supplement). The public database21 includes information on the clinical actionability for each somatic gene variant organized by indication and four-tier levels of evidence (Tables 1 and 2) which are ultimately incorporated into cBioPortal22 for Cancer Genomics to aid physicians and cancer researchers.6 Many genes involved in tumorigenesis are not targetable with currently available drugs. More than 90% of alterations in OncoKB have biologic effects and are classified as oncogenic but not actionable. For actionability, tumor ontology is considered. OncoKB contains 43 tumor types with biomarker-drug associations and uses an open-source ontology, OncoTree,23 which was developed at MSKCC (699 tumor types). The Clinical Genomics Annotation Committee (CGAC) reviews the OncoKB alteration across 22 disease management teams. Curation reviews occur every 3 months, and CGAC recommendations and feedback are updated in real time.6

For this study, levels of evidence 2A and 2B were consolidated to level 2 and levels 3A and 3B to level 3 for both WfG and OncoKB. For actionability annotation comparisons, we mapped ontology from WfG to OncoKB to ensure that gene variants were properly annotated with their respective tumor type. In addition, for actionability annotation comparisons, we excluded microsatellite instability (MSI), as the MSI status was not entered into WfG and we do not have MSI information from WfG.

Among 1,227 NGS results, 1,388 unique variants were observed in 117 genes. The entire set of unique gene variants is shown in the Data Supplement. The genes with the largest number of variants included were: TP53 (270), STK11 (92), CDKN2A (81), ATM (67), PTEN (52), NF1 (46), and BRCA2 (45). The most common cancer was lung adenocarcinoma in 35.86% (440). Other cancer types included colon adenocarcinoma 9.21% (113) and lung squamous cell carcinoma 9.05% (111). The complete list of cancer types is shown in Figure 1.

Of the 1,388 unique gene variants, 1,082 were identified only by PGDx (a larger proportion of the samples were sequenced by PGDx), and 480 were identified only by Personalis, whereas 174 gene variants were generated by both vendors in different specimens. NoO classification should be similar for both vendors. The unique gene variants generated by each vendor are shown in the Data Supplement. The genes included in the gene panels are listed in the Data Supplement. We also list the well-characterized genes of each panel as well as the genes for which copy analysis is performed. For panel distribution by NGS samples, see the Data Supplement. All unique gene variants were annotated by NoO, WfG, and OncoKB. Out of the 1,388 unique gene variants, 337 (24.2%) were variants of unknown significance by OncoKB. Out of the 1,388 unique gene variants, 270 (19.4%) were variants of unknown significance by WfG.

For pathogenicity annotation, (ie, pathogenic and likely pathogenic variants v all other variants), there was fair agreement between WfG and OncoKB (76%; kappa, 0.22) and no agreement between WfG and NoO (30%; kappa, −0.26) as well as NoO and OncoKB (42%; kappa, −0.07; Table 3; Fig 2; Appendix Fig A1).

Table

TABLE 3. Unique Gene Variants and Their Annotation as Pathogenic or Likely Pathogenic Using Three Annotation Services: N-of-One, WfG, and OncoKB

There were 91 unique gene variant–diagnosis combinations identified as having level 1 drug actionability recommendations identified by WfG, OncoKB, or both (not available from NoO). As part of actionability annotation, we mapped diagnosis ontology from WfG to OncoKB for each observed diagnosis (Data Supplement).

There was moderate agreement between WfG and OncoKB (96.9%; kappa, 0.445), with 58 variants identified only by WfG as level 1 and 6 variants identified only by OncoKB as level 1 (Table 4). The complete set of level 1 drug recommendations for WfG and OncoKB are shown in the Data Supplement. When both annotation services had level 1 drug actionability, the recommended drugs were overwhelmingly identical. An example of a unique gene variant with drug actionability concordance is the exon 19 deletion in lung adenocarcinoma, EGFR-L747_S752del, with level 1 evidence for use of erlotinib, afatinib, or gefitinib using both WfG and OncoKB annotation services. Response rates of tumors with EGFR exon 19 deletions at L747 have been reported as high as 83.3%.24

Table

TABLE 4. Level 1 Drug Actionability Comparing IBM WfG and Open Academic Somatic Variant Database OncoKB

Among the 33 unique gene variant diagnoses identified as level 1 by OncoKB, WfG classification was level 1 in 27; for the 6 cases with discordant classification in WfG, 5 cases were lung adenocarcinoma EGFR mutations and a melanoma BRAF-V600R mutation. For the BRAF-V600R mutation in melanoma, WfG had no level 1 recommendations, whereas OncoKB had 5 drug or drug combinations as level 1. BRAF-V600R mutations constitute approximately 3%-7% of all BRAF mutations and were not included in the original BRAF/MEK inhibitors clinical trials.25 Although V600R is not listed on the BRAF/MEK FDA label, National Comprehensive Cancer Network panel guidelines from as early as 2016 consider single-agent BRAF inhibitor monotherapy and BRAF/MEK inhibitor combination therapy an appropriate treatment of all activating BRAF mutations, including V600R, V600D, and others.26 For EGFR mutations in lung adenocarcinoma (EGFR-A750P, EGFR-E746_T751delinsI, EFGR-G719C, EGFR-L861Q, and EGFR-S7681), WfG had no recommendations in 3 variants and had level 2A for EGFR-G719C and level 3B for both EGFR-A740P and EGFR L861Q for drugs afatinib, erlotinib, and gefitinib. For EGFR-L861Q, an uncommon EGFR mutation, the afatinib FDA label expanded to include EGFR-L861Q in January of 2018, which was after our annotation analysis. These approvals were based on findings from the phase 2 LUX-Lung 2 trial as well as the phase 3 LUX-Lung3 and LUX-Lung 6 trials that showed an objective response rate of 66% (95% CI, 47% to 81%).27

Among the 85 unique gene variant diagnoses identified as level 1 by WfG, OncoKB classification was level 1 in 27, and 58 had discordant classification. Most of the gene variant diagnoses (40/58 [68.9%]) with discordant recommendations were mismatch repair (MMR) genes, such as MLH1, MSH2, MSH6, and PMS2. The remaining gene variant diagnoses (18/58; 31.0%) with discordant recommendations included genes CDK4, EGFR, FGFR1, FLT4, KIT, PDGFRA, PIK3CA, POLE, PTEN, TSC1, TSC2, VEGFA, and VHL. For a lung adenocarcinoma case with MLH1-A42, which is part of DNA MMR genes and known to have increased MSI, WfG provide a level 1 recommendation for atezolizumab, nivolumab, and pembrolizumab. Although MMR status predicts the clinical benefit of immune checkpoint blockade in multiple tumor types,28,29 none of these drugs are FDA approved for MLH1 mutants. Similarly, for other MMR gene variants, WfG provided level 1 recommendations in the absence of an FDA on-label indication.

Precision medicine has promoted the development of biomarker-driven treatment strategies. There has been a surge in the genetic testing market, with a 10% annual increase in new genetic tests and a 20% annual increase in gene-based diagnostic tests.15,30 NGS generates massive amounts of data, and different biologic questions require the development of specific bioinformatics pipelines, which are frequently platform specific.30,31 The processing of raw sequence data has a profound effect on patient care and outcomes, and NGS test validation is necessary.32 Until recently, there were no established uniform technical standards for reporting tumor-derived NGS gene panel sequencing results.33 In this analysis, we examine a large number of clinically observed unique gene variants and compare annotations for pathogenicity and actionability through two commercial services, NoO and WfG, as well as an open database, OncoKB.

In efforts to establish guidelines for NGS gene panel interpretation of somatic variants, the Association of Molecular Pathology (AMP) and College of American Pathologists (CAP) issued a joint consensus of classification (Table 5).32,33 The four-tier system has different nomenclature and classification as compared with WfG and OncoKB (Tables 1 and 2). The WfG and OncoKB levels of evidence are similar and compatible, but not identical, to the AMP/CAP/ASCO evidence levels. The OncoKB evidence levels were mapped to the evidence levels for AMP/CAP/ASCO tier I and II variants (Table 5).34 We used the WfG and OncoKB classification systems to compare their levels to each other, and we found that variants classified as pathogenic had a wide range of concordance, from 30% to 76% (Fig 2; Table 3; Appendix Fig A1). In routine clinical practice, only gene variants that are pathogenic or likely pathogenic are considered for actionability, so misclassification in pathogenicity could readily affect provider prescribing. For drug actionability, there was moderate-strength agreement 96.9% (kappa, 0.445) for levels 1. In practice, levels of evidence 1 and 2A both frequently result in use of a targeted drug, so concordance in level 2A may be similar to level 1, and their combination may Boost concordance among annotation services for actionability. However, we expect greater discordance among levels of evidence 2B to 4.

Table

TABLE 5. Guidelines for Evidence-Based Variant Categories by the AMP, CAP, and ASCO Compared With Levels of Evidence for OncoKB and WfG

Although no direct comparisons between NoO, WfG, and OncoKB have been previously performed, WfG has been compared with MTBs.3 The utility of WfG was compared with MTBs by examining 1,018 cases analyzed by the University of North Carolina’s Human-MTB. WfG identified an additional 8 actionable genes (7 of which passed actionability criteria by Human-MTB). Mutations in these 8 genes were identified in 231 and 96 patients out of 703 and 315 patients with already identified actionable and no actionable mutations, respectively.3 Of the 7 actionable genes identified by WfG, 3 had no clinical trial available, and 4 were made potentially eligible for a biomarker-selected clinical trial. The reason for the added genes by WfG was believed to be the opening of several clinical trials within weeks of the WfG analysis. Most genes identified and reclassified as actionable, however, have yet to demonstrate their utility as predictive biomarkers of response to the recommended therapy. These actionable mutations identified by WfG were found retrospectively in 323 patients, of whom only 47 (4.6%) had active disease requiring further therapy. Moreover, none of the patients had treatment altered based on WfG’s recommendation. Cognitive computing may supplement MTBs, and multiple studies have shown concordance of > 90%.35,36 WfG may provide additional decision support in the current era of rapid generation of information from clinical trials.

Currently, existing evidence does not support population-based universal tumor sequencing.37 The SHIVA trial (ClinicalTrials.gov identifier: NCT01771458) examined patients with any metastatic solid tumor refractory to standard treatment and randomly assigned 195 to receive treatment on the basis of molecular profile (n = 99) versus standard treatment (n = 95). Median progression-free survival was 2.3 months (95% CI, 1.7 to 3.8 months) in the experimental group versus 2.0 months (95% CI, 1.8 to 2.1 months) in the control group (hazard ratio, 0.88; 95% CI, 0.65 to 1.19; P = .41). The study used a variety of tumor types and histologies with single molecular alterations as a predictor for efficacy of targeted agents in heavily pretreated patients, possibly reducing their effectiveness. Nevertheless, there were no differences in progression-free survival or overall survival, and off-label use of targeted agents was discouraged.38 The benefit of off-label use of molecularly targeted agents is largely debated in oncology; however, most agree that clinical trial enrollment should be encouraged to identify predictive druggable biomarkers.

Other prospective studies, including EXACT,39 MOSCATO-01 (ClinicalTrials.gov identifier: NCT01566019),40,41 and MD Anderson’s Phase I Clinic42,43 have shown promising results for targeted therapy on previously treated solid tumors. Additional studies examining the efficacy of off-label use of targeted therapy on the basis of NGS test results are underway: ASCO’s Targeted Agent and Profiling Utilization Registry (TAPUR) study (ClinicalTrials.gov identifier: NCT02693535),44 NCI-MATCH (Molecular Analysis for Therapy Choice) trial (ClinicalTrials.gov identifier: NCT02465060),45 and Pediatric MATCH (ClinicalTrials.gov identifier: NCT03155620). Results from NCI-MATCH for patients with PIK3CA mutations treated with taselisib show 0% objective response rate,46 whereas patients with FGFR pathway mutations treated with AZD4547 showed a 5% objective response rate.47 Currently, there are no randomized controlled trial results supporting a universal NGS-based treatment paradigm. Despite this, NGS testing continues to be incorporated into the clinic.48 In a recent survey of 1,281 US oncologists, 75.6% reported using NGS tests to guide treatment decisions. Of these, 34% used them for patients with advanced refractory disease, and 17.5% used them for decisions on off-label use of FDA-approved drugs.49 More than 50% reported that NGS tests were difficult to interpret either often or sometimes, which is a separate but related challenge facing personalized medicine.49 The use of NGS will likely continue to grow, with providers facing uncertainty as to how to integrate its use into the clinic. Studies like ours are critical and further highlight the current shortcomings in precisely interpreting results of NGS gene panels for use in clinical management.

© 2020 by American Society of Clinical Oncology

This work was conducted as a clinical operational analysis of the National Oncology Program Office, Office of Specialty Care Services, Department of Veterans Affairs. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Presented at the ASCO Annual Meeting, Chicago, IL, June 1-5, 2018.

Supported by the Department of Veterans Affairs, Office of Specialty Care Services.

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Evangelia Katsoulakis

Stock and Other Ownership Interests: AbbVie, Alexion Pharmaceuticals, Biogen

Research Funding: Advantagene (Inst)

Neil L. Spector

Stock and Other Ownership Interests: Eydis Bio, Bessor Pharma

Research Funding: Immunolight

Patents, Royalties, Other Intellectual Property: I am on a patent related to my work with the company Immunolight; Patent through Eydis Bio

Michael J. Kelley

Consulting or Advisory Role: AstraZeneca, Eisai, IBM Japan

Research Funding: Bavarian Nordic, Novartis, AstraZeneca, Bristol-Myers Squibb

Other Relationship: IBM

No other potential conflicts of interest were reported.

Veterans Affairs National Precision Oncology Program.

National Precision Oncology Program (NPOP) services are available to all Veterans Affairs (VA) facilities and as of June 2018, over 72 different VA facilities submitted tumor samples. Following sequencing, a formal report of identified genomic aberrations is collated, annotated and included in patient records. The turnaround time is 14 days. NPOP provides a molecular oncology consultation service to assist VA clinicians with treatment decisions, and a case-based education-focused molecular tumor board. The users of NPOP are VA general oncologists and pathologists. Specialized oncologists and molecular pathologists oversee the program.

Panel information.

Panels were constructed to identify base/missense substitutions, insertions/deletions in protein-encoding regions, copy number variations, selected gene fusions/rearrangements and microsatellite instability (only on CancerSELECT 125 panel). There was overlap between the panels and a minimum of 500X DNA sequence coverage was required for all assays.

OncoKB pathogenicity and actionability.

Annotation of variant pathogenicity was executed using OncoKB support and through the API (http://oncokb.org/). Annotation of actionability as defined by the OncoKB Levels of Evidence was executed using OncoKB support and are available through the OncoKB annotator (https://github.com/). Data regarding all variants annotated as actionable per OncoKB at the time of the study are publicly available at https://github.com/.

Panel breakdown of the NGS samples.

The panel breakdown of the 1,227 NGS samples was: Personalis ACE CancerPlus (n = 404); Personal Genome Diagnostics (PGDx) CancerSELECT 125 (n = 286); PGDx CancerSELECT 203 (n = 75); PGDx CancerSELECT 88 (n = 176); PGDx CancerSELECT 64 (n = 1); PGDx unknown panel (n = 285).

1. Hayes DN, Kim WY: The next steps in next-gen sequencing of cancer genomes. J Clin Invest 125:462-468, 2015 Crossref, MedlineGoogle Scholar
2. Kandoth C, McLellan MD, Vandin F, et al: Mutational landscape and significance across 12 major cancer types. Nature 502:333-339, 2013 Crossref, MedlineGoogle Scholar
3. Patel NM, Michelini VV, Snell JM, et al: Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist 23:179-185, 2018 Crossref, MedlineGoogle Scholar
4. Morris LG, Riaz N, Desrichard A, et al: Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7:10051-10063, 2016 Crossref, MedlineGoogle Scholar
5. Tamborero D, Rubio-Perez C, Deu-Pons J, et al: Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 10:25, 2018 Crossref, MedlineGoogle Scholar
6. Chakravarty D, Gao J, Phillips SM, et al: OncoKB: A precision oncology knowledge base. JCO Precis Oncol 10.1200/PO.17.00011 Google Scholar
7. Taylor AD, Micheel CM, Anderson IA, et al: The path(way) less traveled: A pathway-oriented approach to providing information about precision cancer medicine on My Cancer Genome. Transl Oncol 9:163-165, 2016 Crossref, MedlineGoogle Scholar
8. Huang L, Fernandes H, Zia H, et al: The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations. J Am Med Inform Assoc 24:513-519, 2017 Crossref, MedlineGoogle Scholar
9. Dumbrava EI, Meric-Bernstam F: Personalized cancer therapy—leveraging a knowledge base for clinical decision-making. Cold Spring Harb Mol Case Stud. 4:a001578, 2018 Crossref, MedlineGoogle Scholar
10. Griffith M, Spies NC, Krysiak K, et al: CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 49:170-174, 2017 Crossref, MedlineGoogle Scholar
11. Patterson SE, Liu R, Statz CM, et al: The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies. Hum Genomics 10:4, 2016 Crossref, MedlineGoogle Scholar
12. Damodaran S, Miya J, Kautto E, et al: Cancer Driver Log (CanDL): Catalog of potentially actionable cancer mutations. J Mol Diagn 17:554-559, 2015 Crossref, MedlineGoogle Scholar
13. N-of-One: A Qiagen Company. Clinical interpretation solutions. https://n-of-one.com/ Google Scholar
14. Rhrissorrakrai K, Koyama T, Parida L: Watson for Genomics: Moving personalized medicine forward. Trends Cancer 2:392-395, 2016 Crossref, MedlineGoogle Scholar
15. Robson ME, Bradbury AR, Arun B, et al: American Society of Clinical Oncology policy statement update: Genetic and genomic testing for cancer susceptibility. J Clin Oncol 33:3660-3667, 2015 LinkGoogle Scholar
16. Poonnen PJ, Duffy JE, Hintze B, et al: Genomic analysis of metastatic solid tumors in veterans: Findings from the VHA National Precision Oncology Program. JCO Precis Oncol 10.1200/PO.19.00075 Google Scholar
17. Fiore LD, Brophy MT, Turek S, et al: The VA point-of-care precision oncology program: Balancing access with rapid learning in molecular cancer medicine. Biomark Cancer 8:9-16, 2016 Crossref, MedlineGoogle Scholar
18. Personal Genome Diagnostics (PGDx): CLIA certified. https://www.personalgenome.com/ Google Scholar
19. Personalis, Inc: A leading precision medicine company focused on advanced NGS based services for cancer and clinical trials and translational research. https://www.personalis.com/ Google Scholar
20. Fragoso G, de Coronado S, Haber M, et al: Overview and utilization of the NCI thesaurus. Comp Funct Genomics 5:648-654, 2004 Crossref, MedlineGoogle Scholar
21. OncoKB: Precision oncology knowledge base. https://www.oncokb.org/ Google Scholar
22. Cerami E, Gao J, Dogrusoz U, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401-404, 2012 Crossref, MedlineGoogle Scholar
23. OncoTree: Displaying: oncotree_latest_stable. http://oncotree.mskcc.org/#/home Google Scholar
24. Su J, Zhong W, Zhang X, et al: Molecular characteristics and clinical outcomes of EGFR exon 19 indel subtypes to EGFR TKIs in NSCLC patients. Oncotarget 8:111246-111257, 2017 Crossref, MedlineGoogle Scholar
25. Klein O, Clements A, Menzies AM, et al: BRAF inhibitor activity in V600R metastatic melanoma. Eur J Cancer 49:1073-1079, 2013 Crossref, MedlineGoogle Scholar
26. Coit DG, Thompson JA, Algazi A, et al: NCCN guidelines insights: Melanoma, version 3.2016. J Natl Compr Canc Netw 14:945-958, 2016 Crossref, MedlineGoogle Scholar
27. Wu TH, Hsiue EH, Lee JH, et al: New data on clinical decisions in NSCLC patients with uncommon EGFR mutations. Expert Rev Respir Med 11:51-55, 2017 Crossref, MedlineGoogle Scholar
28. Le DT, Uram JN, Wang H, et al: PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372:2509-2520, 2015 Crossref, MedlineGoogle Scholar
29. Le DT, Durham JN, Smith KN, et al: Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357:409-413, 2017 Crossref, MedlineGoogle Scholar
30. Gargis AS, Kalman L, Bick DP, et al: Good laboratory practice for clinical next-generation sequencing informatics pipelines. Nat Biotechnol 33:689-693, 2015 Crossref, MedlineGoogle Scholar
31. Roy S, Coldren C, Karunamurthy A, et al: Standards and guidelines for validating next-generation sequencing bioinformatics pipelines: A joint recommendation of the Association for Molecular Pathology and the College of American Pathologists. J Mol Diagn 20:4-27, 2018 Crossref, MedlineGoogle Scholar
32. Jennings LJ, Arcila ME, Corless C, et al: Guidelines for validation of next-generation sequencing-based oncology panels: A joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn 19:341-365, 2017 Crossref, MedlineGoogle Scholar
33. Li MM, Datto M, Duncavage EJ, et al: Standards and guidelines for the interpretation and reporting of sequence variants in cancer: A joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 19:4-23, 2017 Crossref, MedlineGoogle Scholar
34. Wagner AH, Walsh B, Mayfield G, et al: A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants. Nat Genet (in press) Google Scholar
35. Somashekhar SP, Sepúlveda MJ, Puglielli S, et al: Watson for Oncology and breast cancer treatment recommendations: Agreement with an expert multidisciplinary tumor board. Ann Oncol 29:418-423, 2018 Crossref, MedlineGoogle Scholar
36. Kim YY, Oh SJ, Chun YS, et al: Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. PLoS One 13:e0200100, 2018 MedlineGoogle Scholar
37. West HJ: No solid evidence, only hollow argument for universal tumor sequencing: Show me the data. JAMA Oncol 2:717-718, 2016 Crossref, MedlineGoogle Scholar
38. Le Tourneau C, Delord JP, Gonçalves A, et al: Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): A multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 16:1324-1334, 2015 Crossref, MedlineGoogle Scholar
39. Prager GW, Unseld M, Waneck F, et al: Results of the extended analysis for cancer treatment (EXACT) trial: A prospective translational study evaluating individualized treatment regimens in oncology. Oncotarget 10:942-952, 2019 Crossref, MedlineGoogle Scholar
40. Verlingue L, Malka D, Allorant A, et al: Precision medicine for patients with advanced biliary tract cancers: An effective strategy within the prospective MOSCATO-01 trial. Eur J Cancer 87:122-130, 2017 [Erratum: Eur J Cancer 93:156-157, 2018] Google Scholar
41. Massard C, Michiels S, Ferté C, et al: High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: Results of the MOSCATO 01 trial. Cancer Discov 7:586-595, 2017 Crossref, MedlineGoogle Scholar
42. Tsimberidou AM, Iskander NG, Hong DS, et al: Personalized medicine in a phase I clinical trials program: The MD Anderson Cancer Center initiative. Clin Cancer Res 18:6373-6383, 2012 Crossref, MedlineGoogle Scholar
43. Tsimberidou AM, Wen S, Hong DS, et al: Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: Validation and landmark analyses. Clin Cancer Res 20:4827-4836, 2014 Crossref, MedlineGoogle Scholar
44. Mangat PK, Halabi S, Bruinooge SS, et al: Rationale and Design of the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. JCO Precis Oncol 10.1200/PO.18.00122 Google Scholar
45. Moore KN, Mannel RS: Is the NCI MATCH trial a match for gynecologic oncology? Gynecol Oncol. 140:161-166, 2016 Google Scholar
46. Krop I, Jegede O, Grilley-Olson J, et al: Results from Molecular Analysis for Therapy Choice (MATCH) arm I: Taselisib for PIK3CA-mutated tumors. J Clin Oncol 36:101, 2018 (15_suppl; abstr 101) Google Scholar
47. Chae YK Vaklavas C, Cheng HH, et al: Molecular analysis for therapy choice (MATCH) arm W: Phase II study of AZD4547 in patients with tumors with aberrations in the FGFR. J Clin Oncol 36:2503, 2018 (15_suppl; abstr 2503) Google Scholar
48. Morash M, Mitchell H, Beltran H, et al: The role of next-generation sequencing in precision medicine: A review of outcomes in oncology. J Pers Med 10.3390/jpm8030030 Google Scholar
49. Freedman AN, Klabunde CN, Wiant K, et al: Use of next-generation sequencing tests to guide cancer treatment: Results from a nationally representative survey of oncologists in the United States. JCO Precis Oncol 10.1200/PO.18.00169 Google Scholar
Mon, 31 May 2021 00:57:00 -0500 en text/html https://ascopubs.org/doi/10.1200/PO.19.00118
Killexams : Are Marketers Equipped For The New Empathy Brand Mandate?

A recent Ipsos poll, conducted on behalf of PepsiCo Beverages North America, finds that Americans believe it is now more critical than ever that brands demonstrate empathetic qualities and take action to maintain customer loyalty and support. In response, companies have described empathy as the "brand mandate" from this point forward.

Empathy has become the flavor of the day among marketers, but what does it mean in practice? Ultimately, empathy is about perspective-taking; it's about walking in your customer's shoes to understand their experience and how we can better help them solve problems in their lives. But let’s be clear, brands aren't empathetic; the people who manage the brands have to be empathetic. As the COVID-19 pandemic and racial injustice protests thrust U.S. society into social and economic turmoil, what does this empathy brand mandate mean in practice?

Given the call for empathy came from PepsiCo, we'll use them as an exemplar to compare the consumers of the brand with the people behind the brand. According to Media Mark data, the typical Pepsi drinker came into this pandemic with no better than a high school education and a household income under $40,000. They may well be among the 14.7% of Americans that have lost their jobs during the epidemic or are essential workers, putting their health on the line daily as they face food insecurity and eviction from their homes. In contrast, a PepsiCo marketer has at least a bachelor's degree and, given they work for PepsiCo, it's from a good university. They still have a job, and it will be a well-paid one. During the pandemic, they're likely working from home as non-essential employees. PepsiCo consumers and PepsiCo marketers are experiencing the pandemic in alternate universes. What's needed to bridge the gap between these two universes is the understanding and insight derived from empathy, but we can't assume that marketers naturally have it.

According to influential psychologist Daniel Goleman, empathy is one of the five key components of emotional intelligence – a vital leadership skill. It develops through three stages: cognitive empathy, emotional empathy, and generative empathy. Cognitive empathy is the ability to understand what another person might be thinking or feeling. It need not involve any emotional engagement by the observer. Cognitive empathy is a mostly rational, intellectual, and emotionally neutral ability. Emotional empathy is the ability to share the feelings of another person, and so to understand that person on a deeper level. It's sometimes called "affective empathy" because it affects or changes you. Generative empathy is the most active form of empathy. It involves not only having concern for another person, and sharing their emotional pain, but also taking practical steps to reduce it.

Experts estimate that around 20 percent of the population is genetically predisposed to empathy. Can we assume then that they all found their way to the marketing profession? Surely professions such as health care, teaching, social work, social entrepreneur, veterinarian, psychologist, artist, librarian, and writer may tap the majority of this population. Marketing may scream creativity (think Mad Men and Blackish), and it's recently latched onto computational thinking and data analytics, but can you name one time you've heard someone say, "you know, you're a really empathetic person, you should go into marketing"?

However, Pepsico's marketers may be better equipped than most to empathize with the lives and experiences of their brand's consumers, especially during the pandemic. PepsiCo's 2018 Diversity Report states that 40% of PepsiCo's Global managers are women, and the global promotion rate for women and people of color is 23%. Psychometric test norms tend to show that women typically have higher natural levels of empathy than men. For example, the 60-question "Empathy Quotient" test measures E.Q. on a 0-80 scale, with 80 being the most empathetic possible. In a U.K. validation of the E.Q. with 1716 participants, researchers found that women have an average E.Q. of 48/80 (60%), and men 39/80 (49%), indicating that women are 11% more empathic than men. A shorter test EQ-Short found women scored 26/44 (59%) and men 20/44 (45%), indicating a similar discrepancy. An alternative empathy test (the TEQ – Toronto Empathy Questionnaire) also shows that women can score more highly. Finally, women tend to outperform men on a behavioral empathy test called the 'Eyes Test' (the full name is 'Reading the Mind in the Eyes'), which tests people's empathic ability to read emotions in people's eyes.

Unfortunately, the gender gap in the marketing industry means that men consistently outnumber women at all but the lowest organizational level. This imbalance means that those higher empathy female employees are also typically not in senior decision-making roles. A 2019 survey of the marketing technology and operations industry found that, while more women than men work in a staff role, director and C-Level positions are around twice as likely to be held by men. Low wage martech positions are more likely to see women filling the role. But when wages surpass the $125k mark, these positions can see around twice the number of men than women doing the work. The 2016 Future of Jobs survey by the World Economic Forum calls out this pattern of declining gender representation as seniority rises. The WEF predicted that in 2020 women would make up 37% of junior-level staff, 33% of mid-level staff, and 24% of senior-level staff within the consumer industry. That doesn't leave much capacity for empathy at the top.

Given its emphasis on building a pipeline of strong talent, it may be no coincidence that PepsiCo has committed more than $45 million to combat the impact of Covid-19, providing vital local humanitarian aid and distributing more than 50 million meals worldwide. Unfortunately, most companies can't claim anything near PepsiCo's efforts on diversity or inclusion.

But, as protests rage across America calling for racial justice, let's make this real. Due primarily to developing grassroots campaigns as early as the 1940s and 1950s to increase sales among black consumers, according to MediaMark, black consumers over-index by 145% on the consumption of Pepsi. How equipped are Pepsico's marketers to empathize with the experience of all types of black Americans in this country and authentically communicate with them? And how much pressure will be put on the company’s black college-educated, well-paid employees to develop campaigns representing the experiences and frustrations of all black Americans? Rather than leaning on demographic diversity within the marketing industry, we have to equip all marketers to empathize and connect with consumers of their brands.

Just like the critical thinking, creativity, and analytical thinking skills that are touted as the mark of successful marketers, empathy is a skill. Companies put job candidates through case challenges, digital skills tests, analytics tests, and more. Yet, despite the declaration of empathy as the new holy grail of marketing, no-one measures whether job candidates possess the skill and to what degree. In practice, companies could choose from a variety of scientifically valid tools to measure empathy among potential employees, including self-report questionnaires, behavioral measures, and neuroscientific measures. Some measurement approaches focus more on the affective components of empathy, others focus more on the cognitive components, and some take a multidimensional perspective.

If companies want to have empathetic brands, they need to recruit, hire, and promote empathetic people. To do this, companies need to assess empathy skills during employee recruitment and prioritize them when making hiring decisions. Then they need to follow PepsiCo's lead in closing the gender gap that exists within their company that keeps women outside the realm of influence in the marketing industry.

Mon, 01 Jun 2020 08:00:00 -0500 Gillian Oakenfull en text/html https://www.forbes.com/sites/gillianoakenfull/2020/06/01/are-marketers-equipped-for-the-new-empathy-brand-mandate/ Killexams : What are the predictors of TOMM failure in clinical TBI populations? A retrospective analysis
Objectives:

To determine base rates of invalid performance on the Test of Memory Malingering (TOMM) in patients with traumatic brain injury (TBI) undertaking rehabilitation who were referred for clinical assessment, and the factors contributing to TOMM failure.

Methods:

Retrospective file review of consecutive TBI referrals for neuropsychological assessment over seven years. TOMM failure was conventionally defined as performance <45/50 on Trial 2 or Retention Trial. Demographic, injury, financial compensation, occupational, and medical variables were collected.

Results:

Four hundred and ninety one TBI cases (Median age = 40 years [IQR = 26–52], 79% male, 82% severe TBI) were identified. Overall, 48 cases (9.78%) failed the TOMM. Logistic regression analyses revealed that use of an interpreter during the assessment (adjusted odds ratio [aOR] = 8.25, 95%CI = 3.96–17.18), outpatient setting (aOR = 4.80, 95%CI = 1.87–12.31) and post-injury psychological distress (aOR = 2.77, 95%CI = 1.35–5.70) were significant multivariate predictors of TOMM failure. The TOMM failure rate for interpreter cases was 49% (21/43) in the outpatient setting vs. 7% (2/30) in the inpatient setting. By comparison, 9% (21/230) of non-interpreter outpatient cases failed the TOMM vs. 2% (4/188) of inpatient cases.

Conclusions:

TOMM failure very rarely occurs in clinical assessment of TBI patients in the inpatient rehabilitation setting. It is more common in the outpatient setting, particularly in non-English-speaking people requiring an interpreter. The findings reinforce the importance of routinely administering stand-alone performance validity tests in assessments of clinical TBI populations, particularly in outpatient settings, to ensure that neuropsychological test results can be interpreted with a high degree of confidence.

Sun, 10 Jul 2022 12:00:00 -0500 en text/html https://www.cambridge.org/core/journals/journal-of-the-international-neuropsychological-society/article/abs/what-are-the-predictors-of-tomm-failure-in-clinical-tbi-populations-a-retrospective-analysis/5610410CAD7C1AD97BDB73C97C718894
COG-645 exam dump and training guide direct download
Training Exams List