Implementation of a surgical site infection prevention bundle in gynecologic oncology patients: An enhanced recovery after surgery initiative
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical outcomes pre- and post-implementation of an evidence-informed surgical site infection prevention bundle (SSIPB) in gynecologic oncology patients within an Enhanced Recovery After Surgery (ERAS) care pathway. METHODS: Patients undergoing laparotomy for a gynecologic oncology surgery between January-June 2017 (pre-SSIPB) and between January 2018-December 2020 (post-SSIPB) were compared using t-tests and chi-square. Patient characteristics, surgical factors, and ERAS process measures and outcomes were abstracted from the ERAS® Interactive Audit System (EIAS). The primary outcomes were incidence of surgical site infections (SSI) during post-operative hospital admission and at 30-days post-surgery. Secondary outcomes included total postoperative infections, length of stay, and any surgical complications. Multivariate models were used to adjust for potential confounding factors. RESULTS: Patient and surgical characteristics were similar in the pre- and post-implementation periods. Evaluation of implementation suggested that preoperative and intraoperative components of the intervention were most consistently used. Infectious complications within 30 days of surgery decreased from 42.1% to 24.4% after implementation of the SSIPB (p < 0.001), including reductions in wound infections (17.0% to 10.8%, p = 0.02), urinary tract infections (UTI) (12.7% to 4.5%, p < 0.001), and intra-abdominal abscesses (5.4% to 2.5%, p = 0.05). These reductions were associated with a decrease in median length of stay from 3 to 2 days (p = 0.001). In multivariate analysis, these SSI reductions remained statistically significant after adjustment for potential confounders. CONCLUSION: Implementation of SSIPB was associated with a reduction in SSIs and infectious complications, as well as a shorter length of stay in gynecologic oncology patients.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".