Methadone Death, Dosage and Torsade de Pointes: Risk-Benefit Policy Implications
Bibliographic record
Abstract
Methadone maintenance treatment (MMT) for opioid dependency has consistently shown important heath, social and legal benefits. What started as a small experimental program in Lexington, Kentucky has grown and expanded substantially over 35 years. Its practice is now well established both in specialized centers and in the broader community. In society, methadone deaths represent an important issue of public safety: methadone diversion to and ingestion by nontolerant individuals outside of treatment. Within treatment, methadone deaths occur most commonly in the early stabilization period (due to issue of tolerance), in periods of transition, or among certain individuals who abuse other substances (opioids, benzodiazepines, or alcohol). Research suggests moderately high methadone dosages help improve patient retention. Results from pharmacodynamic, kinetic and stereospecific studies continue to support the importance of individualizing dose. For some patients, much larger doses may be necessary to fully achieve all pharmacotherapy goals of treatment. Practitioners must be cautious however as certain patients on higher dosages are predisposed to torsade de pointes and increased mortality. Policymakers have a responsibility in their decision-making to balance the quality of life benefits for patients within MMT with the risks of increased mortality both for individuals within treatment and the general public.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".