Antibiotic prescriptions for outpatient acute rhinosinusitis in Canada, 2007-2013
Bibliographic record
Abstract
INTRODUCTION: Acute rhinosinusitis (ARS) is a respiratory disease commonly caused by viral infections. Physicians regularly prescribe antibiotics despite bacterial etiologies being uncommon. This is of concern, as this use adds to the selection pressure for resistance. Here we present the descriptive epidemiology of acute rhinosinusitis and corresponding antibiotic prescribing practices by Canadian outpatient physicians from 2007-2013. MATERIALS/METHODS: Diagnosis and antibiotic prescription data for ARS were extracted from the Canadian Disease and Therapeutic Index for 2007 to 2013, and population data were acquired from Statistics Canada. ARS diagnosis and antibiotic prescription rates and frequencies of antibiotic classes were calculated. RESULTS: Eighty-eight percent of patients diagnosed with ARS in 2013 were adults, with a greater rate of antibiotic prescriptions observed among the adults relative to the pediatric patients (1632.9 and 468.6 antibiotic prescriptions per 10,000 inhabitants). Between 2007 and 2013, the ARS diagnosis rate decreased from 596 to 464 diagnoses per 10,000 inhabitants, while the percentage of diagnoses with antibiotic prescriptions at the national level remained stable (87% to 84%). From 2007 to 2013, prescription rates for macrolides decreased from 203.5 to 105.4 prescriptions per 10,000 inhabitants. In 2013, penicillins with extended spectrum were more commonly prescribed compared to macrolides among adult patients (153.5 and 105.4 prescriptions per 10,000 inhabitants, respectively). CONCLUSION: This study is the first to describe physician antibiotic prescribing practices for treatment of ARS in Canada. Results show that antibiotic treatment for ARS represents an area for implementing antimicrobial stewardship, and through it, managing antibiotic resistance. Further work is required to better understand diagnosing practices and treatment criteria for ARS, and use this information to further assist physicians to limit unnecessary antibiotic prescribing practices.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".