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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".