Reducing urinary tract infection in female pelvic surgery: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To compare prebundle versus postbundle implementation urinary tract infection (UTI) rates among inpatients within 6 weeks of clean-contaminated pelvic reconstructive surgery. METHODS: The authors conducted a retrospective cohort study from September 2019 to December 2021 at a tertiary hospital. The bundle strategy included the following: universal preoperative UTI check with treatment if positive, replacing prolonged postoperative voiding trials on the ward with earlier discharge and indwelling catheter removal by a nurse continence advisor the next day, and daily cranberry extract for 6 weeks postoperatively. UTI was defined as positive urine culture (≥100 000 colony-forming unit per mL) in a symptomatic patient. Data analysis involved hypothesis testing and logistic regression. RESULTS: The authors reviewed 132 postbundle inpatient charts and retained 93 for analyses. The results were compared with 204 prebundle inpatient charts. The rate of postoperative UTI decreased from 17.6% in the prebundle group to 6.5% after bundle implementation (P = 0.01). The adjusted odds ratio for postbundle versus prebundle likelihood of UTI was 0.35 (95% confidence interval, 0.13-0.98; P = 0.045). Significantly more postbundle patients compared with prebundle patients were discharged home on the first day postoperatively (76.3% vs. 37.7%, P < 0.001). CONCLUSIONS: A clinical bundle can significantly decrease both UTI rates and hospital stay after pelvic reconstructive surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".