Inappropriate use of antibiotics for acute respiratory tract infections in a rural emergency department.
Bibliographic record
Abstract
INTRODUCTION: Evidence-based reviews and guidelines recommend lowering antibiotic prescription rates for acute respiratory tract infections (ARIs). OBJECTIVE: To determine the number of patients presenting with uncomplicated ARIs at the walk-in emergency department (ED) of a rural community health centre and to determine the antibiotic prescription rate for each type of ARI. METHODS: A one-year retrospective data collection of a rural ED was carried out using MEDITECH and chart review to determine numbers of patients presenting with an ARI; antibiotic prescriptions were recorded according to ARI diagnosis. RESULTS: ARIs accounted for 22% of all patients seen by the ED doctor. In 57% of the ARIs diagnosed, patients were prescribed an antibiotic. Individual rates ranged from 2% for influenza to 100% for pneumonia. A breakdown of rates for each type of ARI is provided. CONCLUSIONS: Antibiotic prescription rates for ARIs remain high, with some ARIs being more inappropriately managed than others. The rate of patients presenting with ARIs to the study ED was higher than in some other EDs, possibly reflecting the problems of recruiting and retaining family doctors in many rural areas, including ours.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".