We've Come A Long Way, Maybe: Recruitment of Women and Analysis of Results by Sex in Clinical Research
Bibliographic record
Abstract
During the last decade, North American policymakers have started to demand more representative research populations. Several papers have suggested that there has been improvement, over the last decade, in the number of studies that include women as subjects, yet these same papers have expressed concern that many investigators omit analysis of data by sex from their research reports. Our study examined all clinical research ethics applications from July 1, 1995, to June 30, 2000, at a tertiary care Canadian university teaching hospital to determine whether the investigator planned to recruit both men and women and whether he or she intended to perform analysis of data by sex. For research studying nonsex-specific conditions, 97.6% of researchers intended to recruit both men and women, yet only 20.2% planned to perform analysis of data by sex. This proportion decreased from 29.9% in 1995-1996 to 16.9% in 1999-2000. Seventy-seven percent of the applications submitted were for studies involving drugs, and only 17% of these nonsex-specific studies planned an analysis of data by sex. The results of this study indicate that although researchers in Canada are aware of the importance of planning to recruit women into clinical trials, more needs to be done to ensure that they plan and perform analyses of data by sex.
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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 | 0.526 | 0.593 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".