269. De-Labeling of Allergies to Β-Lactam Antibiotics (De-LABeL) Program: Development and Pilot of an Inpatient Pediatric Program
Bibliographic record
Abstract
Self-reported β-lactam allergy (BLA) labels are common. The implementation of inpatient penicillin allergy testing in the adult setting has been shown to improve antimicrobial use. The impact of this intervention in the pediatric inpatient setting is unknown. We sought to develop and pilot an inpatient β-lactam allergy delabeling program in a pediatric tertiary-care center. In collaboration with the Allergy and Immunology, Infectious Diseases and Pharmacy Divisions, a De-Labeling of Allergies to Β-Lactams (De-LABeL) program has been developed for integration into routine patient care at the Hospital for Sick Children. The oral provocation challenge (OPC) was chosen as the delabeling intervention and the program has been piloted on the General Pediatric service. An algorithm was created to assist clinicians in identifying appropriate candidates for an inpatient OPC. Reported reactions were risk stratified using a systematic framework. A two-step OPC (10% followed by 90% of a weight-based treatment dose of the potential allergen) was used. Following the OPC, patient families received a letter to take to their primary care provider and pharmacist, to provide communication about the status of their BLA. During the 3-month pilot on the General Pediatric service, 32 children with a BLA label were assessed, and one-third of patients (n = 11, 34.4%) were delabeled. Four families declined the OPC. Nine patients (28.1%) were not eligible for an OPC based on the algorithm. The majority of the remaining patients (n = 6, 18.7%) could not complete the OPC during their admission and were referred for outpatient allergy assessment. All assessments were completed in less than 48 hours from the time of admission. No adverse events were observed during the OPCs. Preliminary data from the De-LABeL program pilot is promising and the feedback from knowledge users has been positive. The next phase will be program implementation hospital-wide. All authors: No reported disclosures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".