Antibiotic Prescribing By Family Physicians For Upper Respiratory Tract Infections
Bibliographic record
Abstract
Feedback, non-antibiotic drug recommendations, and patient factors were examined to develop ways to reduce use of inappropriate antibiotics for Upper Respiratory Tract infections (URTIs). 3,220 encounters for URTIs over six months were reported by 45 family physicians who recorded consecutive patients and noted drugs recommended, diagnosis, and patient characteristics. After two months baseline data collection, physicians received feedback about their own and peers antibiotic prescribing, and the effect of this on their prescriptions was studied.Patients recommended ‘over the counter drugs’ were less likely to be given antibiotics for acute bronchitis (OR, 0.22; 95% CI, 0.13-0.38; P<0.0001), pharyngitis (OR, 0.46; 95% CI, 0.29-0.75; P=0.0001), acute sinusitis (OR, 0.08; 95% CI, 0.03-0.22; P<0.0001), and acute otitis media (AOM) (OR, 0.27; 95% CI, 0.11-0.65; P=0.004). Prescriptions for drugs other than antibiotics were also negatively associated with antibiotics for acute bronchitis (OR, 0.49; 95% CI, 0.31-0.78; P=0.003) and acute sinusitis (OR, 0.29; 95% CI, 0.12-0.72; P=0.007). Adults (OR, 1.8; 95% CI, 1.1-3.0;P=0.03), males (OR, 1.6; 95% CI, 1.0-2.5; P=0.05), and patients with co-morbidity (OR, 2.4; 95% CI, 1.4-4.0; P=0.001) were more likely to be prescribed antibiotics for acute bronchitis.After feedback antibiotic prescribing decreased from 42% to 34% of encounters (χ=16, p<0.0001) and use of the first choice antibiotics recommended in the Ontario guidelines increased from 45% to 56% (χ=10, p=0.002). The results suggest feedback would be an effective means to improve antibiotic prescribing, and recommendations of non-antibiotic therapies would lead to decreased antibiotic use.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".