Virtually supported penicillin allergy de-labelling during COVID-19
Bibliographic record
Abstract
BACKGROUND: Penicillin allergy is a commonly listed medication allergy despite rare overall incidence. Many patients erroneously have this label, which has personal, health, and societal costs. Penicillin allergy delabelling requires an oral challenge, which can be a rate limiting step in the de-labeling process; this is even more relevant with the reduction of in-person visits during the COVID-19 pandemic. OBJECTIVE: To identify the utility and broader applicability of using a virtually supported platform, initially adopted given COVID-19 restrictions, to expedite penicillin oral provocation challenge and penicillin de-labeling in patients at low to moderate risk of immediate hypersensitivity reaction and based on shared decision making. METHODS: Patients in Vancouver catchment area were referred for penicillin allergy and virtually assessed by the consulting allergist between July 2020 and April 2021. Those deemed appropriate for oral challenge based on the allergist consultant were offered the option of a virtual oral provocation challenge to oral amoxicillin in a subsequent virtual visit. Patients who agreed and were consented underwent a virtually supervised oral amoxicillin challenge during the second virtual visit. Findings are summarized in this case series. RESULTS: Twenty-three patients, both adult and pediatric, ranging from no to significant co-morbidities were consented and underwent the virtual challenge. One hundred percent of patients were successful with no reaction after an hour post virtual oral provocation challenge with amoxicillin. CONCLUSION: Virtual medicine is likely to remain in the allergist's practice. Virtually supported penicillin allergy delabelling, based on shared decision making and risk stratification, presents another pathway for penicillin allergy delabelling.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".