Stopping routine urine screening studies for stroke rehabilitation inpatient admissions
Bibliographic record
Abstract
Urine testing on asymptomatic patients is not aligned with guidelines; however, stroke survivors have trouble communicating symptoms, and urinary tract infections (UTIs) are a recognised poststroke complication. All stroke inpatients at a tertiary rehabilitation hospital underwent urine testing on admission. We led a quality improvement (QI) project on one stroke rehabilitation unit aimed to reduce admission urine testing from 100% to 0%. Baseline audit representing 2 weeks of admissions identified 27 of 28 patients had urine tests; however, none required UTI treatment despite 3 positive culture results. Estimated cost of testing was $C675. QI tools identified that a standardised paper-based admission form facilitated automatic urine testing. Project intervention strategies included education, clinicians crossing off urine orders and unit clerks flagging unaddressed orders for reassessment. A chart audit after 4 weeks and prescriber survey after 6 months assessed impact. Postintervention audit (n=23) revealed 1 patient had admission urine tests, 22 orders were crossed out, 1 chart was flagged and estimated testing cost declined from $C675 to $C25. Six urine tests were completed after admission and two patients required UTI treatment. Post 6 months, unit clerks assumed the role to cross out the order on the standardised form, and no patient had routine admission urine testing. There was no clinical benefit in screening for UTIs prior to stroke rehabilitation. This project is a practical example of deadopting a practice promoted by standardised order forms.
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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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".