Race and ethnicity reporting and representation in hemophilia clinical trials
Bibliographic record
Abstract
ABSTRACT: Racial and ethnic representativeness in clinical trials is crucial to mitigate disparities in outcomes; however, diversity among hemophilia trials is unknown. The aim of this study is to examine the reporting and representation of race and ethnicity in trials of people with hemophilia (PwH). In this cross-sectional study, the ClinicalTrials.gov database was queried in April 2023 for interventional clinical trials involving PwH between 2007 and 2022. The distribution of participants (observed) was compared with expected proportions based on US Hemophilia Treatment Center (HTC) and country-specific census data with observed-to-expected ratios (OERs). Of 129 trials included, 94.6% were industry sponsored, with a mean of 62 participants and mean age of 26.8 years. Overall, 52.0% (n = 66) of trials reported data on race and ethnicity, increasing from 13.9% in 2007-2012 to 22.5% in 2013-2016 to 100% in 2017-2022 (P = .001). Among these 66 trials, 65.8%, 22.8%, 5.1%, 3.9% of participants were White, Asian, Hispanic, and Black, respectively. OERs were 10% to 20% lower for White participants vs US HTC, and US, UK, and Canadian census populations and ∼75% lower for Black or Hispanic participants when compared with US HTC and US census population. OERs for Asian participants were 1.6 to 3 times higher than Canada, US, and UK census populations. The reporting of race and ethnicity in hemophilia trials has drastically improved; however, Black and Hispanic PwH remain especially underrepresented. To address these disparities, stakeholders across the clinical trial enterprise need to implement strategies to ensure equitable participation.
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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 | 0.080 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".