Using the Electronic Health Record to Facilitate Drug Allergy Delabeling
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is a growing number of allergy delabeling programs across diverse clinical specialties and care settings. The electronic health record (EHR) can be leveraged to facilitate allergy delabeling. The purpose of this review is to describe EHR tools that have been used in allergy delabeling programs. We also provide recommendations for organizations considering EHR-based allergy delabeling workflows that incorporate clinical informatics best practices. RECENT FINDINGS: Recent literature describes several EHR tools used in delabeling. These tools can be organized around the steps of the allergy delabeling workflow: 1. Identify eligible patients, 2. Risk stratify, 3. Evaluation and testing, 4. Documentation of outcome, 5. Delabeling, and 6. Allergy label reconciliation. Standardized EHR tools across the allergy delabeling workflow can lead to successful delabeling and support of diverse stakeholders. Partnering with EHR vendors presents an opportunity to make these tools readily available and improve allergy documentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".