Determining typical doses of injectable diacetylmorphine and hydromorphone for the treatment of opioid use disorder
Bibliographic record
Abstract
Opioid use disorder (OUD) is a major Public Health concern in Canada. Injectable Opioid Agonist Treatments (iOAT) with diacetylmorphine (DAM) and hydromorphone (HDM) have proven to be safe and effective treatments for OUD. Currently, several sites in British Columbia and across Canada offer iOAT as part of their regular addiction care plan. While effectiveness and safety has been demonstrated, daily program features, such as regular doses, still need to be studied to improve efficiency. Importantly, typical doses of iOAT remain unknown. The aim of this thesis was to determine the most common dosage ranges of iOAT to support program planning. This thesis utilized data from a longitudinal cohort of participants receiving iOAT from August 2014 to April 2020 at the site with the most iOAT clients in Vancouver. Descriptive statistics including interquartile rage and histograms were obtained to illustrate typical doses of iOAT and define dosage groups. Using participants’ data from a double-blind clinical trial, paired t-tests and linear regression were performed to examine differences in double-blind versus open-label iOAT dose received. The open-label potency ratio between DAM and HDM was calculated and described with percentile ranks. A total of 595,305 injections were registered during the study period among 131 participants. Mean DAM doses ranged from 106 – 989 mg per day, with most clients using 150 – 300 mg per session (mean 225 mg) attending 2.7 sessions per day. Mean HDM doses ranged from 51.1 – 696.1 mg per day, the majority using 75 – 150 mg per session (mean 125 mg; 2.8 sessions). A moving average demonstrated that participants’ doses tend to remain stable over the study period. Paired t-test analyses demonstrated that participants received significantly less open-label DAM (-49 mg; p <0.01) but no significant difference in open-label HDM (5 mg; p >0.05). Further testing suggested this difference was attributed to drug preferences. Participants use the entire range of possible prescriptions, demonstrating the diversity and variability across people accessing iOAT, however stability in dosage can be expected. Providing these certainties will help providers to prepare for what they need to offer and facilitate the expansion of iOAT programs across Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".