What Is Driving Paradigm Shifts in Plastic Surgery and Is Cosmetic Surgery Keeping Up?
Bibliographic record
Abstract
BACKGROUND: Cosmetic surgery represents 20 to 30 percent of total plastic surgical volume. The authors hypothesize that with current capitalization and market share, cosmetic surgery should be proportionally represented in scientific innovation. METHODS: All journals that may contain articles relevant to plastic surgery were selected from the 2016 edition of Journal Citation Reports. The authors identified, reviewed, and analyzed the 100 top-cited plastic surgery clinical articles using the Science Citation Index Expanded (1900 to 2017) as a proxy for innovation. RESULTS: The top-100 articles were cited a median of 329.5 times (range, 240 to 1709 times). Sixteen journals were represented, led by Plastic and Reconstructive Surgery (45 percent) and Annals of Surgery (15 percent). Fifty-six percent were reconstructive, 13 percent were breast, 11 percent were pediatric/craniofacial, 11 percent were cosmetic, and 9 percent were hand/peripheral nerve articles. Only 11 percent of articles represented level of evidence I or II, with the majority (79 percent) of articles being level IV. Sixty-seven percent of publications originated from United States. The 11 cosmetic articles originated from different subspecialties: injectables, fillers, and fat grafting (n = 7); contouring (n = 2); facial cosmetic (n = 1); and general cosmetic (n = 1). CONCLUSIONS: Cosmetic innovation is not keeping up with reconstructive innovation; it is unknown why cosmetic surgery is lacking. The authors offer several speculations as to why there is a gap in cosmetic surgical research and, by proxy, innovation.
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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 | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".