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 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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".