Abstract 1290: Racial and ethnic representation and disparities on clinical guideline panels in oncology
Bibliographic record
Abstract
Abstract Purpose: Culturally competent diverse workforce is critical to equitable and progressive cancer care. Yet, racial bias, permeates practice and research in oncology. The National Comprehensive Cancer Network (NCCN) panels recommend guidelines that determine standards for patient care in the United States (US). We investigated the extent of racial/ethnic representation/disparity amid this higher echelon of cancer leadership. Methods: We collected data from publicly available NCCN (https://www.nccn.org) guidelines (version: 1.2023-5.2023) published between 11/2022-11/2023. Six research team members extracted data. Race/ethnicity (NIH categories: White, Black, Hispanic/Latinx, Asian) was identified by advanced AI detection software tools (Namsor and Kairos, utilizing names and facial recognition to infer US race). Gender was determined using name/pronoun (if available). All information was confirmed using online databases (institutional profiles, biographical paragraphs, social media) and group consensus (in case of discrepancies). Data regarding active US physicians was obtained using Association of American Medical Colleges (AAMC) 2022 physician specialty data report. The primary objective was to determine the racial/ethnic composition of panels and association between race/ethnicity and chairs/co-chairs/vice-chairs (lead positions or leads) within panels. Descriptive statistics were used. Proportions were compared using Fisher-exact or Chi-squared test (odds-ratio [OR] and 95% confidence intervals [95%CI] were reported). Results: We reviewed 63 panels corresponding to 63 distinct disease sites. A total of 1223 unique individuals [475 (38.8%) females and 748 (61.2%) males] accounted for 2162 panel members with a median of 34 members (range: 25-42) per panel. Racial/ethnic representation was 1455 (67.3%) Whites, 570 (26.4%) Asians, 93 (4.3%) Hispanics/Latinx, and 44 (2.0%) Blacks, which was comparable to racial/ethnic makeup of active US physicians (64%, 21%, 7% and 6%, respectively). Among these 2162 member positions, 129 (6.0%) were lead positions. In these leads, racial/ethnic representation was 105 (81.4%) Whites, 5 (11.4%) Blacks, 17 (3.0%) Asians, and 2 (2.2%) Hispanics/Latinx. Notably, no significant difference was seen between proportion of female (5.2%) and male (6.4%) leads (OR: 0.80; 95%CI:0.55-1.16; P=0.26). However, the proportion of Whites in lead positions was significantly higher than Non-whites (7.2% vs. 3.4%; OR: 2.2; 95%CI: 1.4-3.5; P < 0.001). Conclusions: Although, overall membership of NCCN panels shows favorable racial/ethnic diversity, underrepresentation and bias appear to subsist for non-White race/ethnicity, when it comes to leadership positions within this key decision-making body that influences cancer care. Further efforts to improve this disparity within oncology is an essential step to shaping equity within the field. Citation Format: Jonathan M. Loree, Arvind Dasari, Mena Shaheed, Himanish Gothwal, Kulwinder Singh, Hewad Shaheed, Riya Mangal, Shivek Gothwal, Jason Willis, Michael J. Overman, Scott Kopetz, Kanwal Pratap Singh Raghav. Racial and ethnic representation and disparities on clinical guideline panels in oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1290.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".