Systematic Review of Women Leading and Participating in Nephrology Randomized Clinical Trials
Bibliographic record
Abstract
Introduction Women are underrepresented in the leadership of and participation in randomized controlled trials (RCTs). We conducted a bibliometric review of nephrology RCTs to examine trial leadership by women and participation of women in nephrology RCTs. Methods A bibliometric review of RCTs published in top medical/surgical/nephrology journals was conducted using MEDLINE and EMBASE from January 2011-December 2021. Leadership by women as corresponding authors, women trial participation, and trial characteristics were examined with duplicate independent data extraction. Logistic regression was used to examine associations between trial characteristics and women leadership and trial participation. Results 1770 studies were screened and 395 RCTs met eligibility criteria. The number (%) of women in corresponding, first, and last authorship positions were: 89 (22%), 109 (28%), and 74 (19%), without change over time (p=0.94). The median percentage (IQR) of women trial participants was 39.0 (13.5)% with no difference between women or men lead authors (p=0.15). Men lead authors were statistically less likely to enroll women in RCTs. Women lead authors were less likely to be funded by industry [OR 0.30, 95% CI (0.14, 0.63), p=0.002] or lead international trials [OR 0.11, 95% CI (0.01, 0.83), p=0.03]. Trials with sex-specific eligibility criteria were more likely to have women leaders [OR 2.56, 95% CI (1.19, 5.49), p=0.02] compared to those without. Discussion Gender inequalities in RCT leadership and RCT participation exist in nephrology and did not improve over time. Strategies to improve inequalities need to be implemented and evaluated.
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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 | 0.110 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.032 | 0.040 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".