Optimizing Surgical Site Infection Prevention in Dermatologic Surgery
Bibliographic record
Abstract
We aim to review modifiable risk factors and practices for surgical site infections (SSIs) reduction in cutaneous surgeries. The existing norms are assessed with the latest evidence, with the aim of enhancing and optimizing intra and postoperative strategies. This review seeks to offer an updated summary of the results of evidence for SSI reduction strategies tailored for practicing general dermatologists. Searches were conducted for “cutaneous surgery surgical site infection complications” using PubMed Central ® and DynaMed ® . Articles with pragmatic guideline recommendations were selected. We found evidence for intraoperative factors such as non-sterile gloves, brushless hand scrubbing/simple hand washing, sterile materials, and chlorhexidine gluconate as a skin antiseptic. For postoperative factors, there is a lack of evidence to support the use of topical antibiotic ointments, dressings, or waiting 48 hours before wetting to prevent SSI. Several intra/postoperative factors not specific to dermatologic procedures are briefly discussed for additional context. Several SSI risk factors are inherent to patients or necessary procedures; however, dermatologists have identified modifiable risk factors and developed protocols to mitigate SSI risks intraoperatively and postoperatively. By questioning established practices in cutaneous surgery aimed at preventing SSIs, we can work towards the optimal utilization of resources. This dual-focused approach not only enhances the efficiency of the healthcare system but also diminishes the risks associated with SSIs. It is important to acknowledge that this review does not encompass all factors essential for consideration in these recommendations. Nonetheless, it will approach these factors with an evidence-based lens, placing SSI prevention at the forefront.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".