Is authorship by women in Brazilian academic surgery increasing? A five-year retrospective analysis
Bibliographic record
Abstract
Women remain underrepresented in 80% of Brazilian surgical specialties, however, women representation within the Brazilian academic surgical literature remains unknown. This study aims to evaluate the gender distribution of first and last authors in Brazilian surgical journals. All publications between 2015 and 2019 from the five Brazilian surgical journals with the highest impact factor were reviewed. The first and last authors' names were extracted from each article and a predictive algorithm was used to classify the gender of each author. Authors were further classified by surgical field and geographic region to investigate patterns of female authorship among journals, specialties, and region over the study period. Multivariable logistic regression was then used to identify factors independently associated with female authorship. 1844 articles were analyzed; 23% (426/1844) articles had female first authors, and 20% (348/1748) had female last authors. Acta Cirúrgica Brasileira was observed to have the highest rates for both first and last female authors (37%, 138/371; 26%, 95/370)) and Revista Brasileira de Ortopedia (9%, 48/542; 10%, 54/522) had the lowest rates. Papers with a woman senior author were twice as likely to have a woman first author (OR 1.98, 95% CI 1.51-2.58, p≤0.01). Women's representation in medicine is increasing in Brazil, yet women remain underrepresented as the first and last authors in the Brazilian surgical literature. Our results highlight the importance of senior women mentorship in academic surgery and demonstrate that promoting female surgeon senior authorship through academic and financial support will positively impact the number of female first authors.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".