Gender Disparity in Leading Authorship of Critical Care Clinical Trials: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
In critical care medicine, research trials serve as crucial avenues for disseminating knowledge, influencing clinical practices, and fostering innovation. Notably, a significant gender imbalance exists within this field, potentially mirrored in the authorship of critical care research. This study aimed to investigate an exploration to ascertain the presence and extent of female representation in first and senior authorship roles within critical care literature. To this end, a systematic search was conducted across PubMed, Google Scholar, and Web of Science databases for original articles published up to February 2024, coupled with a methodological quality assessment via the Newcastle-Ottawa Scale (NOS) and statistical analyses through Review Manager software (RevMan, version 5.4.1, The Cochrane Collaboration, 2020). The study's findings, distilled from seven studies included in the final analysis, reveal a pronounced gender disparity. Specifically, in critical care literature examining mixed populations, female first authors were significantly less common than their male counterparts, with an odds ratio (OR) of 4.25 (95% confidence interval (CI): 3.18-5.68; p < 0.00001). Conversely, pediatric critical care studies did not show a significant difference in gender distribution among first authors (OR: 1.37; 95% CI: 0.31-6.10; p = 0.68). The investigation also highlighted a stark underrepresentation of female senior authors in critical care research across both mixed (OR: 11.67; 95% CI: 7.76-17.56; p < 0.00001) and pediatric populations (OR: 5.41; 95% CI: 1.88-15.56; p = 0.002). These findings underscore the persistent underrepresentation of women in critical care literature authorship and their slow progression into leadership roles, as evidenced by the disproportionately low number of female senior authors.
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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 | 0.095 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".