270 Clicks matter. improving ordering efficiency
Bibliographic record
Abstract
tailored staff education, and the appointment of departmental champions to drive adoption.Protocols defined inappropriate catheterization based on factors such as surgical duration (<180 minutes), expected postoperative bedrest (<24 hours), and thresholds for urinary retention and residuals. 5 7Results A total of 2,711 adult patients were included (2,167 before; 544 after implementation).Following the intervention, the percentage of patients without inappropriate IDUC increased from 46% to 57%, and those without inappropriate CIC from 34% to 67%.Total catheter use also declined: the proportion of patients not receiving an IDUC rose from 54% to 64%, and those without CIC from 89% to 92%.Ordinal logistic regression, adjusted for age, sex, hospital, and surgery type, confirmed statistically significant reductions in total IDUC use (adjusted OR 0.61, 95% CI 0.50-0.76)and inappropriate CIC use (adjusted OR 0.25, 95% CI 0.13-0.51).UTI rates remained stable (1.4% vs. 1.3%), and the average length of hospital stay did not increase (4.9 vs. 5.1 days).Discussion Key factors contributing to success included multidisciplinary buy-in, strong local leadership, and the adaptability of training formats, including online tools necessitated by the COVID-19 pandemic.Challenges involved staff turnover and pre-existing variability in institutional catheter protocols.The role of nurses as key decision-makers in catheter use was expanded, aligning with current literature suggesting nursedriven catheter management improves outcomes. 8 9 This study highlights the potential of structured, scalable strategies to improve the quality and safety of postoperative care.By combining evidence-based protocols with localized implementation, inappropriate catheter use was significantly reduced without compromising patient safety or length of stay.The findings support broader application of this approach to other surgical disciplines or invasive interventions.Sustained adherence will require ongoing training, audit-feedback loops, and integration into hospital-wide quality improvement systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".