Policy makers ignoring science and scientists ignoring policy: the medical ethical challenges of heroin treatment.
Bibliographic record
Abstract
A decade of research in Switzerland, The Netherlands, Germany, and Spain now constitutes a massive body of work supporting the use of heroin treatment for the most difficult patients addicted to opiates. These trials concur on this method's safety and efficacy and are now serving as a prelude to the institution of heroin treatment in clinical practice throughout Europe. While the different sampling and research protocols for heroin treatment in these studies were important to the academic claims about specific results and conclusions that could be drawn from each study, the overall outcomes were quite clear--and uniformly positive. They all find that the use of prescribed pharmaceutical heroin does exactly what it is intended to do: it reaches a treatment refractory group of addicts by engaging them in a positive healthcare relationship with a physician, it reduces their criminal activity, improves their health status, and increases their social tenure through more stable housing, employment, and contact with family. The Canadian trial (NAOMI), now underway for over a year, but not yet completed, now faces a dilemma about what to do with its patients who have successfully completed 12 months of heroin and must be withdrawn from heroin and transferred to other treatments in accordance with the research protocol approved by Government of Canada, federal granting body and host institutions. The problem is that the principal criterion for acceptance to NAOMI was their history of repeated failure in these very same treatment programs to which they will now be referred. The existence of the results from abroad (some of which were not yet available when NAOMI was designed and initiated) now raises a very important question for Canada: is it ethical to continue to prohibit the medical use of heroin treatment that has already been shown to be feasible and effective in numerous medical studies throughout the world? And while this is being worked out, is it acceptable to require patients who have been successfully treated with heroin in Canada, to be forced to move back to less effective treatments (treatments that failed to be efficacious in the past)? This essay discusses this dilemma and places it in the broader context of ethics, science, and health policy. It makes the case for continuation of the current successful patients in heroin treatment and the institution of heroin treatment to all Canadian patients living with active addictions who qualify.
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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 | 0.110 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.083 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.065 | 0.070 |
| Insufficient payload (model declined to judge) | 0.005 | 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".