First pediatric electronic algorithm to stratify risk of penicillin allergy
Bibliographic record
Abstract
Beta-lactam allergy is reported in 5-10% of children in North America, but up to 94-97% of patients are deemed not allergic after allergist assessment. The utility of standardized skin testing for penicillin allergy in the pediatric population has been recently questioned. Oral drug challenges when appropriate, are preferred over skin testing, and can definitively rule out immediate, IgE-mediated drug allergy. To our knowledge, this is the only pediatric study to assess the reliability of a penicillin allergy stratification tool using a paper and electronic clinical algorithm. By using an electronic algorithm, we identified 61 patients (of 95 deemed not allergic by gold standard allergist decision) as low risk for penicillin allergy, with no false negatives and without the need for allergist assessment or skin testing. In this study, we demonstrate that an electronic algorithm can be used by various pediatric clinicians when evaluating possible penicillin allergy to reliably identify low risk patients. We identified the electronic algorithm was superior to the paper version, capturing an even higher percentage of low risk patients than the paper version. By developing an electronic algorithm to accurately assess penicillin allergy risk based on appropriate history, without the need for diagnostic testing or allergist assessment, we can empower non-allergist health care professionals to safely de-label low risk pediatric patients and assist in alleviating subspecialty wait times for penicillin allergy assessment.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".