Fatal overdoses involving hydromorphone and morphine among inpatients: a case series
Bibliographic record
Abstract
BACKGROUND: Opioids have narrow therapeutic windows, and errors in ordering or administration can be fatal. The purpose of this study was to describe deaths involving hydromorphone and morphine, which have similar-sounding names, but different potencies. METHODS: In this case series, we describe deaths of patients admitted to hospital or residents of long-term care facilities that involved hydromorphone and morphine. We searched for deaths referred to the Patient Safety Review Committee of the Office of the Chief Coroner for Ontario between 2007 and 2012, and subsequently reviewed by 2014. We reviewed each case to identify intervention points where errors could have been prevented. RESULTS: We identified 8 cases involving decedents aged 19 to 91 years. The cases involved errors in prescribing, order processing and transcription, dispensing, administration and monitoring. For 7 of the 8 cases, there were multiple (2 or more) possible intervention points. Six cases may have been prevented by additional patient monitoring, and 5 cases involved dispensing errors. INTERPRETATION: Opioid toxicity deaths in patients living in institutions can be prevented at multiple points in the prescribing and dispensing processes. Interventions aimed at preventing errors in hydromorphone and morphine prescribing, administration and patient monitoring should be implemented and rigorously evaluated.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".