Antibiotic Prescribing Choices and Their Comparative <i>C. Difficile</i> Infection Risks: A Longitudinal Case-Cohort Study
Bibliographic record
Abstract
BACKGROUND: Antibiotic use is the strongest modifiable risk factor for the development of Clostridioides difficile infection, but prescribers lack quantitative information on comparative risks of specific antibiotic courses. Our objective was to estimate risks of C. difficile infection associated with receipt of specific antibiotic courses. METHODS: We conducted a longitudinal case-cohort analysis representing over 90% of Ontario nursing home residents, between 2012 and 2017. Our primary exposure was days of antibiotic receipt in the prior 90 days. Adjustment covariates included: age, sex, prior emergency department or acute care stay, Charlson comorbidity index, prior C. difficile infection, acid suppressant use, device use, and functional status. We examined incident C. difficile infection, including cases identified within the nursing home, and those identified during subsequent hospital admissions. Adjusted and unadjusted regression models were used to measure risk associated with 5- to 14-day courses of 18 different antibiotics. RESULTS: We identified 1708 cases of C. difficile infection (1.27 per 100 000 resident-days). Longer antibiotic duration was associated with increased risk: 10- and 14-day courses incurred 12% (adjusted relative risk [ARR] = 1.12, 95% confidence interval [CI]: 1.09, 1.14) and 27% (ARR = 1.27, 95% CI: 1.21,1.30) more risk compared to 7-day courses. Among 7-day courses with similar indications: moxifloxacin resulted in 121% more risk than amoxicillin (ARR = 2.21, 95% CI: 1.67, 3.08), ciprofloxacin engendered 89% more risk than nitrofurantoin (ARR = 1.89, 95% CI: 1.45, 2.68), and clindamycin resulted in 112% (ARR = 2.12, 95% CI: 1.32, 3.78) more risk than cloxacillin. CONCLUSIONS: C. difficile infection risk increases with antibiotic duration, and there are wide disparities in risks associated with antibiotic courses used for similar indications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".