Penicillin de-labelling in vancouver, British Columbia, Canada: comparison of approaches, outcomes and future directions
Bibliographic record
Abstract
BACKGROUND: Inaccurate penicillin allergy labels lead to inappropriate antibiotic prescriptions and harmful patient consequences. System-wide efforts are needed to remove incorrect penicillin allergy labels, but more health services research is required on how to best deliver these services. METHODS: Data was extracted from five hospitals in Vancouver, British Columbia, Canada from October 2018-May 2022. The primary outcomes of this study were to outline de-labelling protocol designs, identify the roles of various healthcare professionals in de-labelling protocols and identify rates of de-labelling penicillin allergies and associated adverse events at various institutions. Our secondary outcome was to describe de-labelling rates for special populations, including pediatric, obstetric and immunocompromised subpopulations. To achieve these outcomes, participating institutions provided their de-labelling protocol designs and data on program participants. Protocols were then compared to find common themes and differences. Furthermore, adverse events were reviewed and percentages of patients de-labelled at each institution and in total were calculated. RESULTS: Protocols demonstrated a high level of variability, including different methods of participant identification, risk-stratification and roles of providers. All protocols used oral and direct oral challenges, heavily involved pharmacists and had physician oversight. Despite the differences, of the 711 patients enrolled in all programs, 697 (98.0%) were de-labelled. There were 9 adverse events (1.3%) with oral challenges with mainly minor symptoms. CONCLUSIONS: Our data demonstrates that de-labelling programs effectively and safely remove penicillin allergy labels, including pediatric, obstetric and immunocompromised patients. Consistent with current literature, most patients with a penicillin allergy label are not allergic. De-labelling programs could benefit from increasing clinician engagement by increasing accessibility of resources to providers, including guidance for de-labelling of special populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".