How to Define and Manage Low-Risk Drug Allergy Labels
Bibliographic record
Abstract
Risk stratification in drug allergy implies that specific risk categories (eg, low, moderate, and high) classify historical drug hypersensitivity reactions. These risk categories can be based on reaction phenotypic characteristics, the timing of the reaction and evaluation, the required reaction management, and individual characteristics. Although a multitude of frameworks have been described in the literature, particularly for penicillin allergy labels, there has yet to be a global consensus, and approaches continue to vary between allergy centers. Immune-mediated drug allergies can sometimes be confirmed using skin testing, but a negative drug challenge is required to demonstrate tolerance and remove the allergy from the electronic health record ("delabel" the allergy). Even for quintessential IgE-mediated drug allergy, penicillin allergy, recent data reveal that a direct oral challenge, without prior skin testing, is an appropriate diagnostic strategy in those who are considered low-risk. Drug allergy pathogenesis and clinical manifestations may vary depending on the culprit drug, and as such, the optimal approach should be based on risk stratification that considers individual patient and reaction characteristics, the likely hypersensitivity reaction phenotype, the drug class, and the patient's clinical needs. This article will describe low-risk drug allergy labels, focusing on β-lactam and sulfonamide antibiotics, nonsteroidal anti-inflammatory drugs, iodinated contrast media, and common chemotherapeutics. This review will also address practical management approaches using currently available risk stratification and clinical decision tools.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".