Efficacy of Strategies Intended to Prevent Surgical Site Infection After Lower Limb Revascularization Surgery
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to evaluate the efficacy of strategies intended to prevent surgical site infection (SSI) after lower limb revascularization surgery. BACKGROUND: SSIs are common, costly complications of lower limb revascularization surgery associated with significant morbidity and mortality. METHODS: We searched MEDLINE, EMBASE, CENTRAL, and Evidence-Based Medicine Reviews (inception to April 28, 2022). Two investigators independently screened abstracts and full-text articles, extracted data, and assessed the risk of bias. We included randomized controlled trials (RCTs) that evaluated strategies intended to prevent SSI after lower limb revascularization surgery for peripheral artery disease. We used random-effects models to pool data and GRADE to assess certainty. RESULTS: Among 6258 identified citations, we included 26 RCTs (n=4752 patients) that evaluated 12 strategies to prevent SSI. Preincision antibiotics [risk ratio (RR)=0.25; 95% CI, 0.11-0.57; n=4 studies; I2 statistic=7.1%; high certainty] and incisional negative-pressure wound therapy (iNPWT) (RR=0.54; 95% CI, 0.38-0.78; n=5 studies; I2 statistic=7.2%; high certainty) reduced pooled risk of early (≤30 days) SSI. iNPWT also reduced the risk of longer-term (>30 days) SSI (pooled-RR=0.44; 95% CI, 0.26-0.73; n=2 studies; I2 =0%; low certainty). Strategies with uncertain effects on risk of SSI included preincision ultrasound vein mapping (RR=0.58; 95% CI, 0.33-1.01; n=1 study); transverse groin incisions (RR=0.33; 95% CI, 0.097-1.15; n=1 study), antibiotic-bonded prosthetic bypass grafts (RR=0.74; 95% CI, 0.44-1.25; n=1 study; n=257 patients), and postoperative oxygen administration (RR=0.66; 95% CI, 0.42-1.03; n=1 study) (low certainty for all). CONCLUSIONS: Preincision antibiotics and iNPWT reduce the risk of early SSI after lower limb revascularization surgery. Confirmatory trials are required to determine whether other promising strategies also reduce SSI risk.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".