P.024 Sex and gender reporting in clinical trials among neurological US Food and Drug Administration approvals
Bibliographic record
Abstract
Background: Sex and gender are related but distinct determinants of disease, treatment response, and research reproducibility whose consideration is increasingly required for research funding. Nevertheless, the quality of sex and gender reporting in neurological randomized controlled trials (RCTs) remains unknown. Methods: This ongoing study of RCTs associated with Food and Drug Administration neurological drug approvals aims to determine the frequency of accurate reporting of RCT participants’ sex and gender. Secondary outcomes include changes in reporting over time and RCT design characteristics. Results: Preliminary analysis included 145 RCTs (153,410 participants) associated with 77 medications approved in 1985-2023, most commonly for epilepsy (19%), migraine (16%), and multiple sclerosis (16%). Sixty-six RCTs (45.5%) used sex-related terms appropriately. Nine RCTs (6.2%) reported gender accurately. Fifty-three RCTs (37%) used sex- or gender-related terms interchangeably. There are no statistically significant differences in the proportions of studies reporting sex and/or gender accurately when comparing those published until versus after 2017. No RCT reported sex or gender collection methods, definitions of sex or gender, or including sex or gender minority participants. Conclusions: Preliminary results suggest shortcomings in reporting sex and, especially, gender accurately and inclusively among neurological drug RCTs and no significant improvement thereof in recent years.
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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 | 0.348 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".