The Urine-culturing Cascade: Variation in Nursing Home Urine Culturing and Association With Antibiotic Use and Clostridiodes difficile Infection
Bibliographic record
Abstract
BACKGROUND: Rates of antibiotic use vary widely across nursing homes and cannot be explained by resident characteristics. Antibiotic prescribing for a presumed urinary tract infection is often preceded by inappropriate urine culturing. We examined nursing home urine-culturing practices and their association with antibiotic use. METHODS: We conducted a longitudinal, multilevel, retrospective cohort study based on quarterly nursing home assessments between April 2014 and January 2017 in 591 nursing homes and covering >90% of nursing home residents in Ontario, Canada. Nursing home urine culturing was measured as the proportion of residents with a urine culture in the prior 14 days. Outcomes included receipt of any systemic antibiotic and any urinary antibiotic (eg, nitrofurantoin, trimethoprim/sulfonamides, ciprofloxacin) in the 30 days after the assessment and Clostridiodes difficile infection in the 90 days after the assessment. Adjusted Poisson regression models accounted for 14 resident covariates. RESULTS: A total of 131 218 residents in 591 nursing homes were included; 7.9% of resident assessments had a urine culture in the prior 14 days; this proportion was highly variable across the 591 nursing homes (10th percentile = 3.4%, 90th percentile = 14.3%). Before and after adjusting for 14 resident characteristics, nursing home urine culturing predicted total antibiotic use (adjusted risk ratio [RR] per doubling of urine culturing, 1.21; 95% confidence interval [CI], 1.18-1.23), urinary antibiotic use (RR, 1.33; 95% CI, 1.28-1.38), and C. difficile infection (incidence rate ratio, 1.18; 95% CI, 1.07-1.31). CONCLUSIONS: Nursing homes have highly divergent urine culturing rates; this variability is associated with higher antibiotic use and rates of C. difficile infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".