Adverse Events During Treatment Induction With Injectable Diacetylmorphine and Hydromorphone for Opioid Use Disorder
Bibliographic record
Abstract
OBJECTIVES: The present study aims to describe a 3-day induction protocol for injectable hydromorphone (HDM) and diacetylmorphine (DAM) used in 3 Canadian studies and examine rates of opioid-related overdose and somnolence during this induction phase. METHODS: The induction protocol and associated data on opioid-related overdose and somnolence are derived from 2 clinical trials and one cohort study conducted in Vancouver and Montreal (2005-2008; 2011-2014; 2014-2018). In this analysis, using the Medical Dictionary for Regulatory Activities coding system we report somnolence (ie, drowsiness, sleepiness, grogginess) and opioid overdose as adverse events. Overdoses requiring intervention with naloxone are coded as severe adverse events. RESULTS: Data from the 3 studies provides a total of 1175 induction injections days, with 700 induction injection days for DAM, and 475 induction injection days for HDM. There were 34 related somnolence and adverse event (AE) overdoses (4.899 per 100 injection days) in DAM and 6 (1.467 per 100 days) in HDM. Four opioid overdoses requiring naloxone (0.571 per 100 injection days) were registered in DAM and 1 in HDM (0.211 per 100 injection days), all safely mitigated onsite. The first week maximum daily dose patients received were on average 433.62 mg [standard deviation (SD) = 137.92] and 223.26 mg (SD = 68.06) for DAM and HDM, respectively. CONCLUSIONS: A 3-day induction protocol allowed patients to safely reach high doses of injectable hydromorphone and diacetylmorphine in a timely manner. These findings suggest that safety is not an evidence-based barrier to the implementation of treatment with injectable hydromorphone and diacetylmorphine.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".