Patient‐dependent risk factors for wound infection after skin surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
Postoperative wound infection in dermatologic surgery causes impaired wound healing, poor cosmetic outcome and increased morbidity. Patients with a high-risk profile may benefit from perioperative antibiotic prophylaxis. The objective of this systematic review was to identify risk factors for surgical site infection after dermatologic surgery. In this article, we report findings on patient-dependent risk factors. The literature search included MEDLINE, EMBASE, CENTRAL and trial registers. We performed meta-analysis, if studies reported sufficient data to calculate risk ratios with 95% confidence intervals. Study quality was assessed according to the Newcastle-Ottawa-Scale. Seventeen observational studies that analysed 31213 surgical wounds were eligible for inclusion. Fourteen studies qualified for meta-analysis. Nine studies showed good, three fair and five poor methodological quality. The reported incidence of surgical site infection ranged from 0.96% to 8.70%. Meta-analysis yielded that male gender and immunosuppression were significantly associated with higher infection rates. There was a tendency towards a higher infection risk for patients with diabetes, without statistical significance. Meta-analysis did not show different infection rates after excision of squamous cell carcinoma or basal cell carcinoma, but studies were substantially heterogenous. There was no significant association between risk for wound infection and smoking, age over 60 years, oral anti-aggregation or anti-coagulation or excision of malignant melanoma. In conclusion, the risk for surgical site infection in dermatologic surgery is low. Infection rates were increased significantly in male as well as immunosuppressed patients and non-significantly in diabetics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".