Development of a Risk Score to Aid With the Diagnosis of Infections After Spinal Cord Injury: Protocol for a Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients with spinal cord injury (SCI) who develop urinary tract infection (UTI) present differently than the non-SCI population. UTIs can cause loss of quality of life and even lead to life-threatening complications including urosepsis. Challenges in SCI management include distinguishing symptomatic UTI from asymptomatic bacteriuria (ASB), which occurs often in patients with SCI, and the lack of standardization in UTI diagnosis in SCI. OBJECTIVE: This study aims to set the foundation for the development of a risk score to improve diagnostic accuracy of UTI after SCI. METHODS: This study will use data from the Veterans Health Administration Corporate Data Warehouse from national outpatient clinics. It will use 2 approaches: (1) a case-control study comparing frequency-matched healthy SCI cases (n=2000) with healthy non-SCI controls (n=2000) to establish a physiologic baseline for vital sign and lab measurements after SCI and (2) a retrospective cohort study of patients with SCI (n=400) to determine the positive predictive value of the baseline vital signs and lab measurements found in step 1, from which a threshold for clinically meaningful UTI after SCI will be established. RESULTS: The study was funded in May 2023, and initial data extraction started in early 2024 and is expected to be completed in 2026. Data extraction, analysis, and results for aim 1 were completed as of manuscript submission. For aim 1, we hypothesize that SCI will be associated with lower temperature, heart rate, and systolic blood pressure when compared with non-SCI controls. SCI will also be associated with higher baseline levels of pyuria and peripheral white blood cells when compared with non-SCI controls. Data extraction for aim 2 will begin in year 1, and analysis and results will be completed in year 2. For aim 2, we hypothesize that pyuria, heart rate, and temperature measurements will have a high positive predictive value for clinically meaningful UTI. CONCLUSIONS: Once complete, this study will be the basis for our future work developing a risk score to aid with the diagnosis of UTI after SCI and prevent antibiotic overuse in patients with SCI. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52610.
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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 | 0.060 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".