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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".