Risk Factors for Surgical Site Infection in Minor Dermatological Surgery: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To identify patient- and procedure-related risk factors for surgical site infection following minor dermatological surgery. DATA SOURCES: The MEDLINE, Cumulative Index of Nursing and Allied Health Literature, Informit, and Scopus databases were searched for relevant literature on patient populations receiving minor surgery, where risk factors for surgical site infection were explicitly stated. STUDY SELECTION: Studies involving major dermatological surgery were excluded. The preliminary search yielded 820 studies after removing duplicates; 210 abstracts were screened, and 42 full texts were assessed for eligibility. A total of 13 articles were included. Studies were appraised using the Newcastle-Ottawa Quality Assessment Scale. DATA EXTRACTION: An electronic data collection tool was constructed to extract information from the eligible studies, and this information was distributed to participating authors. DATA SYNTHESIS: Risk factors identified included age, sex, diabetes mellitus, chronic obstructive pulmonary disease, use of antihypertensive or corticosteroid medications, smoking, surgery on the lower or upper extremities, excision of nonmelanocytic skin cancers, large skin excisions, and complex surgical techniques. No more than two studies agreed on any given risk factor, and there were insufficient studies for meta-analysis. CONCLUSIONS: Re-excision of skin cancer, below-knee excisions, and intraoperative hemorrhagic complications were predictive for infection in more than one study. More high-quality studies are required to accurately identify risk factors so they can be reliably used in clinical guidelines.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".