Conception and development of Urinary Tract Infection indicators to advance the quality of spinal cord injury rehabilitation: SCI-High Project
Bibliographic record
Abstract
Context: Urinary tract infections (UTI) are the most frequent secondary health condition following spinal cord injury or disease (SCI/D) that adversely impact overall health and quality of life, and often result in rehabilitation service interruptions, emergency department visits, and urinary sepsis.Methods: Experts in Urohealth and/or UTI recognition and management and the SCI-High Project Team used a combination of evidence synthesis and consensus methods for developing the UTI indicators. A systematic search and a Driver diagram analysis were applied to identify key factors influencing UTI. This Driver diagram guided the UTI Working Group when defining the construct, specifying the aim for the UTI SCI/D quality indicators, and developing the UTI diagnostic checklist and fever definition.Results: The structure indicator was the proportion of patients with a health care professional (i.e. family physician or urologist) able to follow-up with the patient regarding urine culture and sensitivity results within 48–72 h of collection. The Working Group knowingly adopted a single checklist for UTI diagnosis, recognizing the stark contrast in the complexity of diagnosis in acute versus community settings. The process indicator is the proportion of SCI/D rehabilitation inpatients with UTI as defined by the UTI diagnostic checklist. The outcome indicator is the proportion of SCI/D rehabilitation inpatients with inappropriate antibiotic prescription.Conclusion: UTI can be diagnosed using the developed symptoms and signs checklist. These structure, process, and outcome quality indicators will ultimately reduce inappropriate antibiotic therapy for UTI and the rising incidence of antibiotic resistance among community-dwelling individuals with chronic SCI/D.
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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 | 0.081 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".