Penicillin allergy de-labeling: Adaptation of risk stratification tool for patients and families
Bibliographic record
Abstract
Penicillin allergy is reported in 10% of the population; however, over 90% of patients are deemed non-allergic upon allergist assessment. The goal of this quality improvement project is to validate a patient-driven assessment tool to safely identify patients at low risk of penicillin allergy and de-label them. Pediatric patients and pregnant women referred to the institution's allergy clinics for penicillin allergy assessment were invited to use the patient tool to complete a self-assessment, resulting in the assignment of a risk category. The risk stratification determined using the patient tool was compared against the allergist's assessment. The patient tool demonstrated agreement with the allergist assessment in 57/84 (67.9%, 95% CI [56.7%,77.4%]) assessments, intra-class correlation (ICC) = 0.618, p < 0.001. In 22/84 (26.2%) assessments, the patient tool determined a higher risk category, primarily due to differences in patients' perceived timing and description of symptoms. Only 5/84 (6.0%) patients were placed in a lower risk category by the patient tool compared to the allergist assessment. The patient tool demonstrates good validity in determining penicillin allergy risk, offering potential as a method of empowering patients to advocate in their care. Iterative changes to the patient tool will be applied to increase agreement.
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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 | 0.023 | 0.056 |
| 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 | 0.000 |
| 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 | 0.002 |
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".