Program to remove incorrect allergy documentation in pediatrics medical records
Bibliographic record
Abstract
The incidence of incorrectly reported drug allergies in a pediatrics hospital and the effectiveness of pharmacist interventions to clarify these reports were studied. A four-month prospective study included children (< or = 18 years of age) with at least one drug allergy reported in their medical chart. Drug allergies were assessed by a pharmacist who labeled the reactions as true, incorrectly reported, or undetermined allergies, in accordance with defined criteria. When an incorrectly reported allergy was removed from a patient's chart with the consent of the attending physician, the intervention was reported to the community pharmacist. A total of 186 of 248 drug allergies identified in 1591 patient charts were challenged. Of these, 26 (14%), 103 (55%), and 57 (31%) were considered true, undetermined, and incorrectly reported drug allergies, respectively, by the pharmacist. A total of 53 (93%) incorrectly reported allergies were removed from patients' charts with the consent of the attending physicians. Community pharmacists were contacted in 25 of these cases. At follow-up, the incorrect allergy documentation was found to have been removed from 23 community pharmacy charts. A pharmacist found numerous incorrectly reported allergies in a pediatrics hospital and assisted in removing them from patients' medical charts.
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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 | 0.008 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".