Variables Associated With Medication Errors in Pediatric Emergency Medicine
Bibliographic record
Abstract
OBJECTIVE: Medication errors are a common cause of iatrogenic morbidity and mortality. The incidence of medication errors in pediatric emergency departments (EDs) has not been described. The objective of this study was to describe the incidence and type of drug errors in a pediatric ED and determine factors associated with risk of errors. METHODS: A retrospective cohort study was conducted of the charts of 1532 children who were treated in the ED of a pediatric tertiary care hospital during 12 randomly selected days from the summer of 2000. Two pediatricians, blinded to other study variables, independently decided whether a medication error occurred and ranked it according to a severity score. Disagreement was resolved by consensus. RESULTS: Prescribing errors were identified in 10.1% of the charts. The following variables were associated in univariate analysis with an increased proportion of errors: patients seen between 4 AM and 8 AM (odds ratio [OR]: 2.45; 95% confidence interval [CI]: 1.10-5.50), patients with severe disease (OR: 2.53; 95% CI: 1.18-5.41), medication ordered by a trainee (OR: 1.48; 95% CI: 1.03-2.11), and patients seen during weekends (OR: 1.48; 95% CI: 1.04-2.11). Among trainees, there was a higher rate of errors at the beginning of the academic year (OR: 1.67; 95% CI: 1.06-2.64). Logistic regression revealed increased risk for errors when a medication was ordered by a trainee (OR: 1.64; 95% CI: 1.06-2.52) and in seriously ill patients (OR: 1.55; 95% CI: 1.06-2.26). CONCLUSIONS: In the pediatric ED, trainees are more likely to commit prescribing errors, and the most seriously ill patients are more likely to be subjected to prescribing errors.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".