Bibliographic record
Abstract
The development of medications for treating persons with opioid use disorders has expanded the number of evidence-based treatment options, particularly for persons with the most severe disorders. It has also improved outcomes compared to psychosocial treatment alone and expanded treatment availability by increasing the number of physicians involved in treatment and the settings where patients can be treated. The medications include methadone, buprenorphine, buprenorphine/naloxone, and extended-release injectable naltrexone. Studies have shown that they are most effective when used over an extended, but as-yet-unspecified, period of time and with counseling and other services, particularly for the many with psychosocial problems. Though controversial in some cultures, well-designed studies in Switzerland, the Netherlands, Germany, and Canada have demonstrated the efficacy of supervised heroin injecting for persons who responded poorly to other treatments, and this treatment option has been approved by Switzerland and a few other E.U. countries. The degree to which medication-assisted therapies are available is dependent on many variables, including national and local regulations, preferences of individual providers and their geographical location, treatment costs, and insurance policies. Greater availability of medication-assisted therapies has become a major focus in the U.S. and Canada, where there has been a marked increase in deaths associated with heroin and prescription opioid use. This paper provides a brief summary of these developments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".