Reducing the rate of post-surgical urinary tract infections in orthopedic patients
Bibliographic record
Abstract
Urinary tract infection (UTI) is the fourth leading cause of healthcare-associated infections, with approximately 70%-80% being attributed to the inappropriate use of indwelling catheters. In many cases, indwelling catheters are used inappropriately without any valid indication, creating potentially avoidable and significant patient distress, discomfort, pain and activity restrictions, together with substantial care burden, cost and hospitalisation. In the Division of Orthopedic Surgery at Toronto Western Hospital (TWH), we identified UTI rate reduction as a quality improvement priority. Patients who underwent total hip and knee joint replacements and hip fracture repairs at TWH were monitored for the incidence of UTI and the usage of catheters. The data collected as part of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) revealed UTI rate of 2.1% among 666 patients who were treated between January and June 2016. Data collected through a custom field in the ACS NSQIP workstation further revealed that indwelling catheters were overused, with 55.2% of patients receiving indwelling catheters in the same time period. These data were presented to the orthopaedic leadership group and surgeons at TWH in July 2016 to set the quality improvement target and create the working group. Nursing staff was provided education to strictly follow the institutional catheter-associated UTI prevention guidelines and change ideas based on the guidelines were implemented in July 2016. As a result, the rate of UTI decreased to 1.1% and the use of indwelling catheter decreased to 19.8% among 883 patients who were treated between July 2016 and March 2017. The study indicated that a systematic approach, engaging all front-line staff including nurse educators and nurse practitioners, helps to facilitate implementation of practice changes. We expect that ongoing reminders and education ensure that the changes are sustainable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".