Gender Authorship Trends in the Ophthalmic Plastic and Reconstructive Surgery Literature
Bibliographic record
Abstract
PURPOSE: Despite increasing numbers of women oculoplastic surgeons, they remain underrepresented within the subspecialty. The purpose of this study was to analyze trends in gender authorship within the field of ophthalmic plastic and reconstructive surgery. METHODS: This retrospective observational study sampled articles published in Ophthalmic Plastic and Reconstructive Surgery (OPRS) and Orbit during the years 1985, 1995, 2005, 2015, and 2020. Data reviewed included article type, total number of authors, and the gender of each article's first and senior author. RESULTS: Nine hundred ninety-nine articles were analyzed, including 701 in OPRS and 298 in Orbit. Of 3,716 total authors, 1,151 (31%) were women, including 297 (29.7%) first authors, and 191 (21.5%) senior authors. Women authorship in OPRS in 1985 (first, 3.9%; senior, 3.3%; all, 3.2%) significantly increased by 2020 (first, 44.6%; senior, 27.9%; all, 42%). Women authorship in Orbit in 1985 (first, 0%; senior, 4.5%; all, 7.4%) also significantly increased by 2020 (first, 43.3%; senior, 34%; all, 42.9%). In a subanalysis of OPRS original investigations alone, women first authorship increased from 3.1% in 1985 to 35.8% in 2020 (p < 0.001) and women senior authorship increased from 4.3% in 1985 to 25% in 2020 (p = 0.001). In a subanalysis of Orbit original investigations alone, women first authorship increased from 0% in 1985 to 65.4% in 2020 (p < 0.001) and women senior authorship increased from 5.3% in 1985 to 42.3% in 2020 (p < 0.001). CONCLUSIONS: Despite a significant increase in women authorship over the past several decades, women remain underrepresented within the oculoplastic literature, particularly in regard to senior authorship. When considering original investigations alone, there has been a significant increase in women first and senior authorship in both OPRS and Orbit.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".