Incisional Negative Pressure Wound Therapy for Surgical Site Infection Prophylaxis in the Post-Antibiotic Era
Bibliographic record
Abstract
Abstract Background: With the prospect of antibiotic failure in the post-antibiotic era, strategies that prevent surgical site infection (SSI) are increasingly important. Current literature suggests that incisional Negative Pressure Wound Therapy (iNWPT) is a promising intervention. Methods: Based on published literature regarding iNPWT, its mechanisms of action, and clinical results, a narrative summary was built, including both the experimental as well as the clinical literature. Results: The experimental literature indicates that iNPWT provides a barrier against external contamination before re-epithelialization, increases blood flow and lymphatic clearance, and reduces edema. Meta-analyses of randomized studies indicate that iNWPT is effective in reducing SSI. We did not identify studies that assessed bacterial clearance during iNPWT in contaminated surgical sites, nor did we identify clinical studies that specified they omitted concomitant antibiotic prophylaxis. Conclusions: Moderate quality evidence indicates that iNWPT reduces SSI, although data without the concomitant use of antibiotic prophylaxis are lacking. The iNPWT is likely effective as a result of its barrier function and optimization of the surgical site micro-environment. For now, iNPWT is recommended for incorporation in SSI prevention bundles. The iNPWT as a substitute for antibiotic prophylaxis is not recommended currently. Further reduction of SSI by iNPWT will lessen the need for therapeutic use of antibiotic agents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".