Gender and Authorship in Pediatric Critical Care Randomized Control Trials*
Bibliographic record
Abstract
OBJECTIVES: To examine the gender distribution of authorship of pediatric critical care randomized control trials. DATA SOURCES: The 415 randomized control trials in pediatric critical care published before 2019. STUDY SELECTION: We included all randomized control trials enrolling children in a PICU. We used PICUtrials.net, which uses comprehensive search strategies of multiple databases, to identify published randomized control trials. DATA EXTRACTION: We manually extracted the name and profession of each listed author from each publication and classified each author as male or female based on their name. RESULTS: We included 2,146 authors and were able to classify 1,888 (88%) as men or women. Overall, 38% of authors were women, this varied with the authorship position: 37% of first, 38% of middle, and 25% of last authors were women (p < 0.001). The three most common professions were physician (63%), nonclinician (11%), and nurse (6%)-of which 30%, 45%, and 97%, respectively, were women. The percentage of female authorship overall has increased from 28% in 1985-1989 to 39% in 2015-2018 (p for trend = 0.004). There were no significant differences in the characteristics of randomized control trials published with a female first or last author versus those with both male first and last authors with respect to the median number of children randomized (60 vs. 50; p = 0.41), multicentred trials (17% vs. 24%; p = 0.12), trials at low risk of bias (50% vs. 66%; p = 0.26), reporting any funding (55% vs. 51%; p = 0.66), or median number of citations per year (1.5 vs. 2.4; p = 0.09). CONCLUSIONS: Although increasing over time, the percentage of researchers publishing pediatric critical care randomized control trials who are women still lags behind the percentage clinicians who are women. Trials that female researchers publish are similar in characteristics and impact as male researchers. Further work should identify barriers to gender diversity and potential solutions in pediatric critical care research.
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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 | 0.132 | 0.510 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".