Addressing Women's Health
Bibliographic record
Abstract
Disparities in women's health and health care increasingly fuel concerns, prompt studies, and advance international policies.The World Medical Association has consistently highlighted the need to address inequities in women's health care, and the World Health Organization continues to include improvements to women and children's health prominently among its goals (World Health Organization, 2009; World Medical Association, 2002, 2008).Until recently, women's health suffered from a void in knowledge to appropriately inform practice and medical policies (Institute of Medicine, 2010), despite the fact that women represent the largest segment of the world population.Until the post-World War II era, male subjects were exclusively used in animal or human biomedical experiments and drug trials.The results of these studies were extrapolated to female, elderly, and child patients.For example, common medications used in men were administered to children, but in smaller doses, and aspirin was administered to women to prevent heart attacks.In 1990, the National Institutes of Health (NIH) required the inclusion of female subjects in clinical trials and biomedical research.Unfortunately, inadequate knowledge and neglect persists in many world medical communities, even after the mandatory inclusion of women in clinical trials.The U.S. Government Accountability Office finds that the Federal Drug Administration does not now have appropriate management systems to monitor how many women are in clinical trials, to be assured that NDAs and IND annual reports are in compliance with pertinent regulations for presenting outcome data by sex and tabulating the number of women included in ongoing trials, or to confirm that its medical officers have adequately addressed sex-related issues in their reviews.While FDA has taken some promising initial steps to address these deficiencies, it is important that the agency finalize the pilot programs it has underway and give sustained
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".