Implementation of Evidence-Based Practices for Surgical Site Infection Prophylaxis: Results of a Pre- and Postintervention Study
Bibliographic record
Abstract
BACKGROUND: Although evidence-based guidelines for best practices pertaining to surgical site infection (SSI) prophylaxis exist, the feasibility of implementing such practices remains to be demonstrated outside of a controlled clinical trial. This study was designed to assess the safety and feasibility of implementing evidence-based care practices to prevent SSIs. STUDY DESIGN: A prospective, double-cohort (pre- and postintervention) trial in elective, general surgery patients was conducted. All patients undergoing elective, major colorectal or hepatobiliary operations were enrolled. Postintervention cohort patients were exposed to new strategies to improve antibiotic administration times, perioperative normothermia rates, and perioperative glucose control. They were compared with the preintervention cohort, which received standard practice at the time. Outcomes evaluated include timing of antibiotic administration, perioperative temperatures, and postoperative glucose levels. SSI rates between cohorts were also compared. RESULTS: A total of 208 patients were enrolled. The proportion of patients receiving their preoperative antibiotics within 60 minutes improved from 5.9% to 92.6% (p < 0.001); perioperative normothermia rates improved from 60.5% to 97.6% (p < 0.001) between cohorts. There was no improvement in rates of hyperglycemia. SSI rates improved but did not reach statistical significance (14.3% versus 8.7%; p = 0.21). CONCLUSIONS: Implementation of evidence-based care practices to prevent SSI is both safe and practical outside the setting of a randomized, controlled trial. Sustained compliance remains to be demonstrated, although practice audits at our institution suggest ongoing success is possible.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".