Barriers and solutions in women’s health research and clinical care: a call to action
Bibliographic record
Abstract
It is now recognized that there are significant differences between the sexes affecting prevalence, incidence, and severity over a broad range of diseases, although the extent of the differences is not fully elucidated. Until the early 1990s, women were excluded from most clinical trials and the limited research including women focused primarily on diseases affecting fertility and reproduction. For these reasons, the prevention, diagnosis, and treatment of chronic diseases in women continue to be based primarily on historical findings in men, and sex-specific clinical guidelines are often lacking. Many illnesses, ranging from cardiovascular disease to cancer to mental health issues, for example, differ by sex in terms of prevalence and adverse effects. Research is needed to understand how medically relevant biological sex differences optimally inform sex-specific prevention, diagnosis, and treatment strategies for women and men. In this way, sex-specific clinical guidelines can be developed where warranted, using evidence-based data.
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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 | 0.256 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.023 | 0.047 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.033 | 0.042 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".