Fluoroquinolone Use and Seasonal Patterns of Ciprofloxacin Resistance in Community-Acquired Urinary Escherichia coli Infection in a Large Urban Center
Bibliographic record
Abstract
Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".