Kidney function and the use of nitrofurantoin to treat urinary tract infections in older women
Bibliographic record
Abstract
BACKGROUND: The antibiotic nitrofurantoin is commonly used to treat uncomplicated urinary tract infections. However, when this drug is used by patients with reduced kidney function, its urine concentration may be subtherapeutic. METHODS: We conducted a population-based study of older women (mean age 79 years) in Ontario, Canada, whose estimated glomerular filtration rate was relatively low (median 38 mL/min per 1.73 m(2)) and for whom 1 of 4 antibiotics had been prescribed for urinary tract infection: nitrofurantoin, ciprofloxacin, norfloxacin or trimethoprim-sulfamethoxazole. We assessed 2 measures of treatment failure in the subsequent 14 days: receipt of a second antibiotic indicated for urinary tract infection and hospital encounter (emergency department visit or hospital admission) with a urinary tract infection. We repeated the analysis for older women with relatively high estimated glomerular filtration rate (median 69 mL/min per 1.73 m(2)). RESULTS: The baseline characteristics of the 4 antibiotic groups were similar. Relative to nitrofurantoin, the other antibiotics (including ciprofloxacin) were associated with a lower rate of treatment failure among women with relatively low estimated glomerular filtration rate (for ciprofloxacin v. nitrofurantoin: second antibiotic prescription, 130/1989 [6.5%] v. 516/3739 [13.8%], odds ratio [OR] 0.44, 95% confidence interval [CI] 0.36-0.53; hospital encounter, 21/1989 [1.1%] v. 95/3739 [2.5%], OR 0.41, 95% CI 0.25-0.66). However, a similar risk of treatment failure with nitrofurantoin was also observed among women with relatively high estimated glomerular filtration rate. The results were consistent in multiple additional analyses. INTERPRETATION: In this study, the presence of mild or moderate reductions in estimated glomerular filtration rate did not justify avoidance of nitrofurantoin.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".