Regional variation in the potentially inappropriate first-line use of fluoroquinolones in Canada as a key to antibiotic stewardship? A drug utilization review study
Bibliographic record
Abstract
BACKGROUND: Serious adverse effects of fluoroquinolone antibiotics have been described for more than decade. Recently, several drug regulatory agencies have advised restricting their use in milder infections for which other treatments are available, given the potential for disabling and possibly persistent side effects. We aimed to describe variations in fluoroquinolone use for initial treatment of urinary tract infection (UTI), acute bacterial sinusitis (ABS), and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the outpatient setting across Canada. METHODS: Using administrative health data from six provinces, we identified ambulatory visits with a diagnosis of uncomplicated UTI, uncomplicated AECOPD or ABS. Antibiotic exposure was determined by the first antibiotic dispensed within 5 days of the visit. RESULTS: We identified 4,303,144 uncomplicated UTI events among 2,170,027 women; the proportion of events treated with fluoroquinolones, mostly ciprofloxacin, varied across provinces, ranging from 18.6% (Saskatchewan) to 51.6% (Alberta). Among 3,467,678 ABS events (2,087,934 patients), between 2.2% (Nova Scotia) and 11.2% (Ontario) were dispensed a fluoroquinolone. For 1,319,128 AECOPD events among 598,347 patients, fluoroquinolones, mostly levofloxacin and moxifloxacin, ranged from 5.8% (Nova Scotia) to 35.6% (Ontario). The proportion of uncomplicated UTI and ABS events treated with fluoroquinolones declined over time, whereas it remained relatively stable for AECOPD. CONCLUSIONS: Fluoroquinolones were commonly used as first-line therapies for uncomplicated UTI and AECOPD. However, their use varied widely across provinces. Drug insurance formulary criteria and enforcement may be a key to facilitating better antibiotic stewardship and limiting potentially inappropriate first-line use of fluoroquinolones.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".