Examining Antibiotic Prescribing and Urine Culture Testing for Urinary Tract Infections (UTIs) in a Primary Care Spinal Cord Injury (SCI) cohort
Bibliographic record
Abstract
Abstract Study Design: A retrospective cohort study Objectives: To describe antibiotic prescribing and urine culture testing patterns for urinary tract infections (UTIs) in a primary care Spinal Cord Injury (SCI) cohort.Setting: A primary care electronic medical records (EMR) database in Ontario.Methods: Using linked EMR health administrative databases to identify urine culture and antibiotic prescriptions ordered in primary care for 432 individuals with SCI from January 1 ,2013 to December 31, 2015. Descriptive statistics were conducted to describe the SCI cohort, and physicians. Regression analyses were conducted to determine patient and physician factors associated with conducting a urine culture and class of antibiotic prescription. Results: The average annual number of antibiotic prescriptions for UTI for the SCI cohort during study period was 1.9. Urine cultures were conducted for 58.1% of antibiotic prescriptions. Fluroquinolones and nitrofurantoin were the most frequently prescribed antibiotics. Male physicians and international medical graduates were more likely to prescribe fluroquinolones than nitrofurantoin for UTIs. Early-career physicians were more likely to order a urine culture when prescribing an antibiotic. No patient characteristics were associated with obtaining a urine culture or antibiotic class prescription.Conclusion: Nearly 60% of antibiotic prescriptions for UTIs in the SCI population were associated with a urine culture. Only physician characteristics, not patient characteristics, were associated with whether or not a urine culture was conducted, and the class of antibiotic prescribed. Future research should aim to further understand physician factors with antibiotic prescribing and urine culture testing for UTIs in the SCI population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".