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.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".