Opioid Medication Errors in Pediatric Practice: Four years’ Experience of Voluntary Safety Reporting
Bibliographic record
Abstract
BACKGROUND: Opioids are the most common source of drug error that leads to harm in pediatric hospitals. OBJECTIVE: To undertake a comprehensive review of experience with voluntary safety reports describing pediatric opioid medication errors at The Hospital for Sick Children (Toronto, Ontario), and to characterize the specific opioids involved, severity and type of error described, hospital location and time of day that the error occurred. METHODS: All medication-related safety reports submitted to an anonymous, voluntary electronic safety reporting database in a university-affiliated pediatric hospital during the first four years of its use were examined. A database of opioid error reports was created for further analysis. RESULTS: A total of 5,935 medication-related safety reports were collected, 507 of which described opioids. Morphine was the most frequently reported opioid, administration was the most frequently reported stage of the medication process (192 errors) and surgical wards were the location from which opioid error was most frequently reported (128 reports). Twenty-two reports described patient harm requiring urgent treatment and intervention. Errors with codeine or hydromorphone resulted in the most significant harm reported. A total of 162 reports described problems with inappropriate opioid disposal, missing opioids, or incorrect opioid counts and checks. CONCLUSIONS: Future opportunities for improvement in opioid safety should focus on morphine, opioid administration errors in general, the safe disposal of opioids in the hospital environment and the identification of pain as an adverse event.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".