Racial and ethnic enrollment disparities in clinical trials leading to Food and Drug Administration approvals for gynecologic malignancies
Bibliographic record
Abstract
BACKGROUND: Compared to White women, Black women and other minority groups have a higher age-adjusted incidence risk of cervical and endometrial cancer. However, the extent of racial and ethnic disparities in clinical trial enrollment among studies performed mainly in North America and Europe for gynecologic malignancy is unknown. OBJECTIVE: This study analyzed enrollment rates by race/ethnicity in trials that led to Food and Drug Administration approvals for gynecological cancers from 2010 to 2024. STUDY DESIGN: This cross-sectional study examined clinical trials registered with ClinicalTrials.gov that resulted in new Food and Drug Administration approvals for gynecologic malignancies between 2010 and 2024. Exclusion criteria were studies not conducted in North America or Europe. Enrollment fractions were obtained by dividing the number of trial participants segregated by the racial/ethnic group by the corresponding U.S. cancer prevalence (uterine, ovarian, and cervical cancer) for 2016 to 2020 for each racial/ethnic group. Odds ratios and 95% confidence intervals were calculated to compare enrollment fractions of minority groups to non-Hispanic Whites. RESULTS: A total of 31 studies met the inclusion criteria, with 21 reporting race/ethnicity data. Three (3/21) studies dichotomized race as non-Hispanic White and non-White and 7 (7/21) reported ethnicity. The median number of participants was 494 [interquartile range 150-674]. Fifteen studies were phase III, and 6 were phase IB/II trials. Treatments included immune checkpoint inhibitors (7 studies), poly (ADP-ribose) polymerase inhibitors (5), vascular endothelial growth factor inhibitors (4), antibody-drug conjugates (4), and an imaging marker (1). Across all studies, 11,258 patients were included, 5563 (49.4%) in ovarian cancer studies, 2963 (26.3%) in endometrial cancer studies, and 2732 (24.3%) in cervical cancer studies. Three studies (n=1734) dichotomized participants into non-Hispanic White and non-White; non-Hispanic White 1291 [74.4%] and non-White 443 [25.6%], and enrollment fractions were 0.51% for non-Hispanic White and 0.43% for no-White, with non-White being underrepresented odds ratio 0.85, 95% confidence interval [0.76-0.95], P=.004. In an Analysis of 18 studies reporting race categories, non-Hispanic Black patients were significantly underrepresented (odds ratio 0.50, 95% confidence interval [0.45-0.54], P<.001), while Asian patients were overrepresented (odds ratio 2.81, 95% confidence interval [2.64-2.99], P<.001). In the 4 studies reporting ethnicity, Hispanic patients were also significantly underrepresented (odds ratio 0.69, 95% confidence interval [0.61-0.78], P<.001). CONCLUSION: In clinical trials, performed in North America and Europe mainly, leading to Food and Drug Administration approvals for gynecologic malignancies, non-Hispanic Black and Hispanic patients are significantly underrepresented compared to non-Hispanic White participants when enrollment is benchmarked to the U.S. female population with gynecological cancer. These trials do not adequately reflect the U.S. populations diagnosed with these malignancies. Enrollment strategies to increase diversity are urgently needed to ensure clinical trial results are equitable and applicable across all populations. Efforts from the American Society of Clinical Oncology, the Association of Community Cancer Centers, and the Gynecologic Oncologic Group/Society of Gynecologic Oncology Inclusion, Diversity, Equity, and Access initiative provide a comprehensive framework for achieving this goal.
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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 | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".