Level of Evidence in Plastic Surgery Research
Bibliographic record
Abstract
BACKGROUND: There has been a recent shift toward evidence-based medicine in the medical and surgical literature. The objective of this study was to determine the level of evidence of published plastic surgery articles. METHODS: A review of the following four major plastic surgery journal publications was performed to determine the level of evidence utilized in the published studies: Plastic and Reconstructive Surgery (PRS), Annals of Plastic Surgery (Annals), Journal of Plastic, Reconstructive, and Aesthetic Surgery (JPRAS), and American Journal of Aesthetic Surgery (Aesthetic) from January 1 to December 31, 2007. RESULTS: Of the 1759 articles reviewed, 726 (41 percent) were included (animal studies, cadaver studies, basic science studies, review articles, instructional course lectures, and correspondence were excluded). The articles were ranked according to their level [level I (highest evidence, e.g., randomized-controlled trials) to level IV (lowest evidence, e.g., case reports)]. The average level of evidence in each journal was as follows: PRS, 3.05; Aesthetic, 3.11; JPRAS, 3.35; and Annals, 3.31. The evidence differed significantly between journals (p < 0.05), except when JPRAS was compared with the Aesthetic journal. Only 2.2 percent of articles were level I evidence. CONCLUSIONS: The average level of evidence in four major plastic surgery journals was 3.2 (level III). In order for the plastic surgery profession to become a participant in higher-level evidence-based medicine, greater emphasis must be placed on prospective randomized blinded trials.
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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 | 0.053 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".