Opioid medication doses among safer supply clients: Current safer supply doses and previous OAT experience
Bibliographic record
Abstract
Introduction: Safer opioid supply (SOS) is a harm reduction approach to prescribing pharmaceutical opioids to people at high risk of overdose from the toxic unregulated drug supply. Previous research demonstrates positive health outcomes and reductions in overdose mortality among SOS clients; however few reports describe previous opioid agonist treatment history prior to initiating SOS, or the medication combinations and doses prescribed within SOS programs. Methods: We used convenience sampling to collect survey data from 95 SOS program clients in London, Canada. We use descriptive statistics to analyze survey data and report on OAT history prior to initiating SOS, including maximum methadone dose. We also report on current SOS medication combinations and doses. Findings: Previous experience with OAT was common and reported by 87 % of SOS clients. Mean highest dose of methadone ever received was 95 mg (range: 20-200 mg), with close to 40 % reporting doses of ≥ 120 mg. 95 % of SOS clients reported prescriptions for immediate-release tablet hydromorphone; 28 % were receiving hydromorphone monotherapy; 68 % were receiving hydromorphone alongside a long-acting opioid, and 5 % receiving hydromorphone alongside 2 long-acting opioids. Total average milligram morphine equivalent (MME) doses of combination SOS prescriptions (MME 1616) were similar to high dose methadone (120 mg = MME 1440). Conclusions: Previous high dose OAT experience was common among SOS clients prior to enrollment in the SOS program. Our results may inform the individualization of high dose opioid prescriptions for people with high tolerance due to exposure to unregulated fentanyl.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".