Optimizing practices to prevent urinary tract infection after cystoscopy and urodynamics in women: A quality improvement study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to reduce the incidence of urinary tract infection (UTI) in women undergoing outpatient cystoscopy and/or urodynamic studies (UDS) at our centre by identifying and then altering modifiable risk factors through an analysis of incidence variability among physicians. METHODS: This was a quality improvement study involving adult women undergoing outpatient cystoscopy and/or UDS at an academic tertiary urogynecology practice. Prophylactic practices for cystoscopy/UDS were surveyed and division and physician-specific UTI rates following cystoscopy/UDS were established. In consultation with key stakeholders, this delineated change concepts based on associations between prophylactic practices and UTI incidence, which were then implemented while monitoring counterbalance measures. RESULTS: Two "Plan-Do-Study-Act-Cycles" were conducted whereby 212 and 210 women were recruited, respectively. Change concepts developed and implemented were: (1) to perform routine urine cultures at the time of these outpatient procedures, and (2) to withhold routine prophylactic antibiotics for outpatient cystoscopy/UDS, except in patients with signs of cystitis. There was no change in the incidence of early presenting UTI (9.0% vs. 9.2%, p = 0.680), but there were significantly fewer antibiotic-related adverse events reported (8.5% vs. 1.5%, p = 0.001). There was no significant change in the total incidence of UTI rates between cycles (7.8% vs. 5.6%, p = 0.649). CONCLUSIONS: No specific strategies to decrease the incidence of UTI following outpatient cystoscopy/UDS were identified, however, risk factor-specific antibiotic prophylaxis, as opposed to universal antibiotic prophylaxis, did not increase UTI incidence.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".