The Effectiveness and Efficacy of Prescribed Diacetylmorphine (Heroin) in Reducing Drug-related Harm
Bibliographic record
Abstract
Opioid overdoses have dramatically increased throughout the past 20 years. Overdoses and other harms associated with the use of the unregulated opioid supply have resulted in a consortium of approaches to reduce drug-related harms, which for decades has included heroin-assisted treatment, although there remains widespread reticence to implement this approach in spite of ample evidence to support its effectiveness. Heroin-assisted treatment is often reserved for persons who have attempted standard opioid agonist treatments - such as methadone - unsuccessfully in order to be eligible for heroin-assisted treatment in countries and regions where available. To date, heroin-assisted treatment is only available in nine countries, mostly in Europe. Heroin-assisted treatment has higher retention rates than other forms of opioid agonist treatments, is cost-effective, reduces overdose morbidity and mortality, and improves public order. Nonetheless, regulatory structures impede its implementation. The present chapter herein presents further details of the evidence on heroin-assisted treatment and newer treatment modality iterations, such as injectable opioid agonist treatment and safe opioid supply programs.&nbsp;<br>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".