The Food and Drug Administration Office of Women's Health: Impact of Science on Regulatory Policy: An Update
Bibliographic record
Abstract
The U.S. Food and Drug Administration Office of Women's Health (FDA OWH) has supported women's health research for ∼20 years, funding more than 300 studies on women's health issues, including research on diseases/conditions that disproportionately affect women in addition to the evaluation of sex differences in the performance of and response to medical products. These important women's health issues are studied from a regulatory perspective, with a focus on improving and optimizing medical product development and the evaluation of product safety and efficacy in women. These findings have influenced industry direction, labeling, product discontinuation, safety notices, and clinical practice. In addition, OWH-funded research has addressed gaps in the knowledge about diseases and medical conditions that impact women across the life span such as cardiovascular disease, pregnancy, menopause, osteoporosis, and the safe use of numerous medical products.
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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 | 0.041 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".