Tenfold Medication Errors: 5 Years’ Experience at a University-Affiliated Pediatric Hospital
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Tenfold medication errors are a significant source of risk to pediatric patients. This may be because of wide variations in age, weight, dosing ranges, and off-label practices, but few studies exclusively devoted to examining pediatric 10-fold error have identified the circumstances and mechanisms that lead to such errors. We examined all 10-fold medication errors reported within an academic, university-affiliated pediatric hospital to make recommendations for future initiatives that could improve medication safety in pediatric practice. METHODS: We retrospectively evaluated all medication-related incident reports submitted to a voluntary safety-reporting database over a 5-year period for reports describing 10-fold medication error. Main outcome measures comprised severity of error, drugs and drug classes involved, 10-fold medication error enablers, mechanisms, and contributing causes. RESULTS: From 6643 medication-related safety reports, 252 10-fold medication errors were identified at a mean reporting rate of 0.062 per 100 total patient days. Morphine was the most frequently reported medication, and opioids were the most frequently reported drug class. Twenty-two reports described patient harm. Intravenous formulations, paper ordering, and drug-delivery pumps were frequent error enablers. Errors of dose calculation, documentation of decimal points, and confusion with zeroes were frequent contributing causes to 10-fold medication error. CONCLUSIONS: This study exclusively and comprehensively examined 10-fold medication errors over a prolonged time in pediatric inpatients. We discuss recommendations of vigilance for specific drugs and standardized order sets for opioids and antibiotics, and identify the administering phase of the medication process as a high-risk practice that can result in pediatric 10-fold medication error.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".