Women as Authors of Randomized Controlled Trials of Minimally Invasive Surgery: Systematic Review and Meta-Analysis of 3 Decades of Trials
Bibliographic record
Abstract
BACKGROUND: Female authorship opportunities have lagged behind those of their male counterparts, with gender disparities most prominent in surgical specialties. Our objective was to determine trends of female first, last, and first or last authorships across time and surgical specialties and whether female first or last authorship was associated with journal impact factor. STUDY DESIGN: A systematic review of EMBASE (OvidSP), MEDLINE (OvidSP), and Cochrane (Wiley) databases from inception to December 22, 2017 was performed to identify all randomized controlled trials evaluating minimally invasive surgery vs classical surgical techniques. The primary end point was female first, last, and first or last authorship, with gender determined via an online search strategy and verified via Genderize.io. Secondary end point was journal impact factor, recorded from Clarivate Analytics InCites. RESULTS: = 0.40, p < 0.001) over time was identified. This trend was observed across surgical specialties except for orthopaedics. The highest calculated percentages of female first, last, and first or last authorships by the year 2017 were seen in obstetrics and gynecology (33.8%, 32.0%, and 43.8%, respectively), all significantly lower than the corresponding percentage of the female obstetrics and gynecology workforce in 2017 (57.0%). Neither female first nor last authorship positions were associated with journal impact factor. CONCLUSIONS: A significant increase in female first and last authorship in randomized controlled trials of minimally invasive surgical techniques in the last 3 decades has been observed, but continued efforts to bridge this gender gap are sorely needed.
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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 | 0.073 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".