Representation of sex, race, and ethnicity in pivotal clinical trials for dermatological drugs
Bibliographic record
Abstract
It is of paramount importance that clinical trials are designed with adequate health equity considerations to prevent disproportionate analyses of specific demographics. In this study, we investigated the representation of sex, race, and ethnicity in pivotal clinical trials for drugs with dermatological disease indications approved by the U.S. Food and Drug Administration between 1995 and 2019. Thirty-six novel drugs with indications to treat dermatological diseases, approved by the U.S. Food and Drug Administration between January 1995 and December 2019 were abstracted from [email protected] The drug approval label, statistical review, official record, and trial publication were reviewed for data on disease indication, approval year, pathway, number of participants, participant demographics (sex, race, and ethnicity), location, and sponsor type. The overall female representation was 45.6% (n = 17,492 of 38,320). Adequate female representation was noted for five of six disease indications. Caucasians were predominantly overrepresented (80.4%; n = 28,065 of 34,890); Blacks (9.8%; n = 3242 of 33,240) and Asians (5.5%; n = 1535 of 27,696) were consistently underrepresented. Across sponsor types, there was a significant difference in the distribution of women (χ2 = 6.332; p = .042), as well as Caucasians (χ2 = 12.813; p = .002), Blacks (χ2 = 13.002; p = .002), and Hispanics/Latinos (χ2 = 7.747; p = .021). Persistence of disparities disproportionately affect the quality of data behind therapies for certain demographics; as such, enrollment practices must continue to address the issue of underrepresentation. Efforts to facilitate demographic equity among clinical trial participants must be supported to ensure that safety and efficacy conclusions are drawn from representative population samples.
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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 | 0.050 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".