Procedure-Related Risk Factors for Surgical Site Infection in Dermatologic Surgery
Bibliographic record
Abstract
BACKGROUND: Identifying risk factors is essential for preventing surgical site infections (SSIs) in dermatologic surgery. OBJECTIVE: To analyze whether specific procedure-related factors are associated with SSI. METHODS: This systematic review of the literature included MEDLINE, EMBASE, CENTRAL, and trial registers. The Newcastle-Ottawa Scale was used for risk bias assessment. If suitable, the authors calculated risk factors and performed meta-analysis using random effects models. Otherwise, data were summarized narratively. RESULTS: Fifteen observational studies assessing 25,928 surgical procedures were included. Seven showed good, 2 fair, and 6 poor study quality. Local flaps (risk ratio [RR] 3.26, 95% confidence intervall [CI] 1.92-5.53) and skin grafting (RR 2.95, 95% CI 1.37-6.34) were associated with higher SSI rates. Simple wound closure had a significantly lower infection risk (RR 0.34, 95% CI 0.25-0.46). Second intention healing showed no association with SSI (RR 1.82, 95% CI 0.40-8.35). Delayed wound closure may not affect the SSI rate. The risk for infection may increase with the degree of preoperative contamination. There is limited evidence whether excisions >20 mm or surgical drains are linked to SSI. CONCLUSION: Local flaps, skin grafting, and severely contaminated surgical sites have a higher risk for SSI. Second intention healing and probably delayed wound closure are not associated with postoperative wound infection.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".