An Analysis of Racial Diversity in the Breast Reconstruction and Aesthetic Surgery Literature
Bibliographic record
Abstract
Background: Racial disparities in the visual representation of patients in the plastic surgery literature can contribute to health inequities. This study evaluates racial diversity in photographs published in the aesthetic and breast reconstruction literature. Methods: A photogrammetric analysis of plastic surgery journals from the USA, Canada, and Europe was performed. Color photographs depicting human skin, pertaining to breast reconstruction and aesthetic surgery in 2000, 2010, and 2020, were categorized as White (1–3) or non-White (4–6) based on the Fitzpatrick scale. Results: All journals demonstrated significantly more White skin images than non-White for all procedures (P < 0.05) except blepharoplasty and rhinoplasty. Blepharoplasty was the only procedure with more non-White images (P = 0.02). When examining USA journals, significant differences were not found in blepharoplasty, rhinoplasty, and male chest surgery. European journals published a greater proportion of non-White images than USA journals (P < 0.0001). There was a decreasing rate of change in diversity with 15.5% of images being non-White in 2000, 32.7% in 2010, and 40.7% in 2020 (P < 0.01). Percentage of non-White images varied by geographical region and ranged from 3.6% in Oceania to 93.5% in Asia (P < 0.01). Conclusions: Diversity of patient populations depicted in plastic surgery literature has increased over the past two decades. Despite this improvement, the racial diversity seen in photographs published in the literature does not adequately reflect this demographic for aesthetic and breast procedures. Equitable visual representation may promote cultural competency and improve care for the populations we serve.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".