Effect on antibiotic prescribing of repeated clinical prompts to use a sore throat score: lessons from a failed community intervention study.
Bibliographic record
Abstract
OBJECTIVES: Infections with group A streptococcus (GAS) occur in 10% to 20% of patients with sore throats, whereas antibiotics are prescribed 50% of the time. Clinical scoring rules can more accurately predict the likelihood of GAS infection, but whether family physicians will adopt such approaches is unclear. This study sought to determine whether repeated clinical prompts to use a scoring approach could help family physicians lower antibiotic use in patients with a sore throat. STUDY DESIGN: Randomized trial in which physicians were assigned to use either (1) chart stickers that prompted them to calculate a score based on clinical findings and provided management recommendations linked to score totals or (2) a clinical checklist. POPULATION: Ninety-seven family physicians in Ontario, Canada, assessed 621 children and adults with sore throat and obtained a throat swab for culture. OUTCOMES MEASURED: (1) Unnecessary antibiotic prescriptions given to patients with a negative throat culture and (2) overall antibiotic use. RESULTS: There were no differences between the control and intervention group in unnecessary antibiotic prescriptions (16.1% vs 20.4%, respectively, P =.29) or overall antibiotic use (27.9% vs 28.1%, P =.97). However, a number of physicians dropped out of the study; as a result, the characteristics of the physicians in the 2 groups were dissimilar in factors related to prescribing. After adjusting for these differences and patient clustering by physician, the odds ratio for the effect of the intervention on unnecessary antibiotic prescriptions was 0.76 (95% confidence interval [CI] = 0.42, 1.40) and 0.57 for overall antibiotic use (95% CI = 0.27, 1.17). CONCLUSIONS: Chart prompts during clinical encounters to use a clinical score in the assessment of patients with a sore throat did not reduce unnecessary antibiotic prescribing by family physicians. The problems encountered in conducting this community-based intervention trial are discussed in relation to the negative result.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".