An Investigation of Gender Representation and Collaboration in Academic Plastic Surgery Research
Bibliographic record
Abstract
Background: Gender disparities in academic leadership positions may be influenced by several factors, including research productivity. We aimed to describe the publication gender gap in major plastic surgery journals, assess gender-related and gender-neutral research publications, and identify any potential gender disparities associated with publication characteristics. Methods: For this cross-sectional study, we reviewed all original research publications in Plastic and Reconstructive Surgery , JAMA Facial Plastic Surgery, and Aesthetic Surgery Journal from 2014 through 2018. Genderize.io was used to identify the gender of all authors. Each publication was classified as either gender-neutral, transgender health, women’s health, or men’s health-related based on the article's content. Results: Of the 12,718 authors across 2234 publications analysed, females were first authors in 30%, last authors in 17%, and all authors in 27%. Among the publications, 1782 (79.8%) were focused on gender-neutral, 419 (18.8%) on women's health, 18 (0.8%) on transgender health, and 15 (0.7%) on men's health. Male first authors were more likely to be associated with women's and transgender health articles (OR [95% CI] = 1.4 [1.1-1.8] and OR [95% CI] = 51.0 [47-55], p < .001) and had a higher mean number of citations compared to gender-neutral articles ( p < .001). Male first authors were more likely to be associated with women's and transgender health articles (OR [95% CI] = 1.4 [1.1–1.8] and OR [95% CI] = 51.0 [47–55], p < .001) and had a higher mean number of citations compared to gender-neutral articles ( p < .001). Conclusion: The publication gender gap persists in academic plastic surgery. The academic community should continue to prioritize addressing gender disparity from the perspective of research productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".