Decreasing Surgical Site Infections in Plastic Surgery: A Systematic Review and Meta-analysis of Level 1 Evidence
Bibliographic record
Abstract
BACKGROUND: Although many interventions are implemented to prevent surgical site infections (SSIs) in plastic surgery, their supporting evidence is inconsistent. OBJECTIVES: The goal of this study was to assess the efficacy of methods for decreasing SSIs in plastic surgery. METHODS: A systematic review and meta-analysis were performed to compare the effects of SSI prevention methods. All the studies were assessed for quality of evidence according to the GRADE assessment. RESULTS: Fifty Level 1 randomized controlled trials were included. The most common interventions for preventing SSIs were antibiotic prophylaxis, showering, prepping, draping, and the use of dressings. Current evidence suggests that antibiotic prophylaxis is largely unnecessary and overused in many plastic surgical procedures, with the exception of head and neck oncologic, oral craniofacial, and traumatic hand surgeries. CONCLUSIONS: Efficacy of antibiotic prophylaxis in plastic surgery is dependent on surgery type. There is a lack evidence that showering and prepping with chlorohexidine and povidone reduces SSIs.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".