Penicillin allergies and community antibiotic prescriptions: A secondary analysis of prescribing choices in six family medicine clinics in southern Ontario, 2018–2019
Bibliographic record
Abstract
Background: While 10% of the population has a reported penicillin allergy, 90% of these allergy labels may not be accurate. However, the impact of penicillin allergy de-labelling on antibiotic use in primary care is not clear. We aimed to determine the effect of penicillin allergy labels on antibiotic use in the community for common infections. Methods: A secondary analysis of cross-sectional data from a previous stewardship intervention study in six family medicine clinics in southern Ontario was conducted. Antibiotic prescriptions to 914 adult patients presenting with common respiratory and urinary tract infections from 2018 to 2019, with and without a penicillin allergy in their electronic medical record, were examined. The primary outcome was what antibiotics were prescribed to patients with and without a penicillin allergy. Results: = 0.01) compared with patients without a penicillin allergy label. If all penicillin allergy patients underwent testing and were successfully de-labelled, we estimated that 40.3/914 (4.4%) of antibiotic prescriptions to adults in this study would change. Conclusions: Antibiotic prescribing choices were affected by penicillin allergy labels, with more frequent prescribing of broad-spectrum antibiotics, particularly in acute respiratory infections. However, the proportion of all antibiotic prescriptions that would be changed by penicillin de-labelling was small. Other antimicrobial stewardship approaches in addition to penicillin allergy de-labelling may be needed to reduce antibiotic overuse in primary care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".