Article Factors Influencing Gender Disparities in Senior Authorship of Plastic Surgery Publications
Bibliographic record
Abstract
INTRODUCTION: Female plastic surgeons publish fewer and lower impact articles. To better understand how to address this gender gap, we explored the temporal trends in female senior authorship and evaluated predictive factors for female senior authorship. METHODS: A retrospective review of articles published in the 3 highest impact plastic surgery journals published from 2010 to 2020 was conducted. Trends with female senior authorship across time were analyzed with respect to study type, subspeciality, and geographical origin. RESULTS: Of the 5425 articles included, 13% (n = 720) had a female senior author, and female senior authorship increased across time ( R = 0.84, P = 0.033). Over the decade, an increased proportion of cohort studies ( R = 0.82, P = 0.045), systematic reviews ( R = 0.96, P = 0.003), breast-related articles ( R = 0.88, P = 0.022), and reconstruction-related articles ( R = 0.83, P = 0.039) were published by female senior authors. Subspecialty and geography predicted female senior authorship; articles focused on aesthetic (odds ratio [OR] = 1.3, P = 0.046) and breast (OR = 1.7, P < 0.001) subspecialties or those originating from Canada (OR = 1.7 P = 0.019), Europe (OR = 1.5, P < 0.001), and Latin America (OR = 3.0, P < 0.001) were more likely to have a female senior author. Articles from East Asia were less likely to have female senior authors (OR = 0.7, P = 0.005). CONCLUSION: Female senior authorship in plastic surgery has increased over the last decade, and the proportion of female plastic surgeons leading cohort studies and systematic reviews is increasing. Sex of the senior author is influenced by plastic surgery subspecialty and geographical origin, but article type did not impact the odds of female senior authorship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".