Decreasing Prescribing Errors During Pediatric Emergencies: A Randomized Simulation Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a clinical aid providing precalculated medication doses decreases prescribing errors among residents during pediatric simulated cardiopulmonary arrest and anaphylaxis. METHODS: A crossover randomized trial was conducted in a tertiary care hospital simulation center with residents rotating in the pediatric emergency department. The intervention was a reference book providing weight-based precalculated doses. The control group used a card providing milligram-per-kilogram doses. The primary outcome was the presence of a prescribing error, defined as a dose varying by ≥20% from the recommended dose or by incorrect route. Residents were involved in 2 sets of paired scenarios and were their own control group. Primary analysis was the difference in mean prescribing error proportions between both groups. RESULTS: Forty residents prescribed 1507 medications or defibrillations during 160 scenarios. The numbers of prescribing errors per 100 bolus medications or defibrillations were 5.1 (39 out of 762) and 7.5 (56 out of 745) for the intervention and control, respectively, a difference of 2.4 (95% confidence interval [CI], -0.1 to 5.0). However, the intervention was highly associated with lower risk of 10-fold error for bolus medications (odds ratio 0.27; 95% CI, 0.10 to 0.70). For medications administered by infusion, prescribing errors occurred in 3 out of 76 (4%) scenarios in the intervention group and 13 out of 76 (22.4%) in the control group, a difference of 13% (95% CI, 3 to 23). CONCLUSIONS: A clinical aid providing precalculated medication doses was not associated with a decrease in overall prescribing error rates but was highly associated with a lower risk of 10-fold error for bolus medications and for medications administered by continuous infusion.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".