Evidence in the Aesthetic Surgical Literature over the Past Decade
Bibliographic record
Abstract
BACKGROUND: Over the past decade, the concepts of evidence-based medicine have become commonplace in surgery. The authors aimed to categorize level of evidence in the aesthetic surgical literature over three intervals during a 10-year period, and to compare this to other surgical specialties. The authors also aimed to assess the quality and predictor factors of higher level evidence. METHODS: Clinical aesthetic surgical literature published in the highest impact journals in 2000, 2005, and 2009/2010 was reviewed. Articles were evaluated for journal, date of publication, number and origin of authors, area, centers of collaboration, number of subjects, study subtype, and level of evidence. Eligible level I studies were evaluated using the Detsky Quality Scale. RESULTS: Five thousand eighty-eight articles were screened, and 526 met eligibility criteria. Thirteen studies (2.5 percent) were level I, 72 (13.7 percent) were level II, 57 (10.8 percent) were level III, 263 (50 percent) were level IV, and 121 (23 percent) were level V. Detsky Quality Scale scores averaged 68.4 percent (minimum, 40 percent; maximum 85 percent). Publications of larger sample size (p = 0.01) and published in Plastic and Reconstructive Surgery (p = 0.02) were significantly associated with higher levels of evidence (levels I/II). The ratio of level I evidence to other levels (levels II to V) in aesthetic surgery compared favorably with oral and plastic surgery; however, ratios were eightfold, sixfold, and fivefold less than those reported in ophthalmology, otolaryngology, and orthopedic surgery, respectively. CONCLUSIONS: Over the past decade, the mean level of evidence in the aesthetic literature has improved. However, level I evidence is the least represented, and these studies have methodologic limitations.
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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 | 0.057 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.040 | 0.034 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".