Enhancing quality practice for prevention and diagnosis of urinary tract infection during inpatient spinal cord rehabilitation
Bibliographic record
Abstract
OBJECTIVES: To reduce the incidence of Urinary Tract Infection (UTI) in subacute SCI individuals admitted for tertiary inpatient rehabilitation. DESIGN: A quality improvement team was assembled to improve UTI prevention/diagnosis. To plan data collection, UTI-related factors were mapped in an Ishikawa (fishbone) driver diagram. Data including patient demographics, presence and frequency of signs and/or symptoms of UTI and antibiotic initiation from August to December 2015 were recorded. Sensitivity, Specificity, Positive and Negative Predictive Values (PPV, NPV), and Likelihood Ratios (LR) were calculated for each sign and symptom. SETTING: Tertiary SCI Rehabilitation Results: Among 55 inpatients with subacute SCI who had signs/symptoms prompting urine culture and sensitivity (C&S), 32 (58.18%) were diagnosed with a UTI. The most frequent symptoms were foul smelling urine (41%), change in urine color (31%), and incontinence (25%), and the most common sign was fever (34%). Most UTIs (81%) occurred among individuals using Clean Intermittent Catheterization (CIC), with 46% of catheterizations performed by nurses. Foul smelling urine had the highest sensitivity (0.50, 95% CI: 0.31-0.69), and new incontinence had the highest specificity (0.88, 95% CI: 0.69-0.97) for UTI diagnosis. The highest PPV belonged to the cloudy urine (0.71, 95% CI: 0.42-0.92). The combination of cloudy and foul smelling urine increased the PPV to 78% (95% CI: (0.40-0.97). CONCLUSIONS: The concurrent presence of cloudy and foul smelling urine is predicted of UTI diagnosis inpatients tertiary setting. SCI inpatients are susceptible to UTI when learning CIC technique from nurses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".