Antimicrobial resistance in urinary «Escherichia coli» in Quebec, Canada
Bibliographic record
Abstract
Background: Urinary tract infections caused by the bacteria Escherichia coli are among the most common infections in the world. Resistance to the antimicrobials used to treat these infections is a growing concern. Given the lack of new drug development, it is critically important to understand the factors underlying patterns of antimicrobial resistance.Methods: The individual-level predictors of resistance to six antimicrobials (ampicillin, gentamicin, ciprofloxacin, nitrofurantoin, trimethoprim/sulfamethoxazole, tobramycin) were investigated in community-acquired and nosocomial urinary E. coli isolates from three cities in the province of Quebec, Canada between April 2010 and December 2017. Hierarchical logistic regression models were used to account for correlations among the six types of resistance. We employed time series analysis in the form of dynamic linear models to explore the temporal association between oral fluoroquinolone use and ciprofloxacin resistance in isolates from the city of Montreal. Fluoroquinolone use in Montreal was estimated using a 25% sample of individuals insured under the public drug prescription plan up to December 2014.Results: Both community-acquired and nosocomial isolates showed geographic variability in the prevalence of resistance. Male sex and recent hospitalization were predictors of increased resistance for most types of resistance; additionally, ciprofloxacin resistance increased sharply with age. Distinct seasonal patterns were noted for community-acquired and nosocomial infections, and resistance in the community setting has been rising since 2015. In Montreal, we found a positive correlation between total fluoroquinolone use lagged by 1 and 2 months and the monthly proportion of isolates resistant to ciprofloxacin.Conclusions: These results demonstrate that hierarchical modelling of the prevalence of, and risk factors for, many types of antimicrobial resistance allows general and region-specific inference, which may inform empirical therapy. The observed correlation between fluoroquinolone use and ciprofloxacin resistance supports the rationale for antimicrobial stewardship campaigns to reduce fluoroquinolone prescriptions in the community setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".