Treatment of opioid dependence
Bibliographic record
Abstract
Editor's note Opiate addiction is one misuse and dependence disorder for which we have good pharmacological treatments. Both methadone and buprenorphine are effective in withdrawing individuals from opiates via substitution, and their use in maintenance treatment has an excellent track record. Naltrexone may also be efficacious for selected patients. Psychosocial and behavioral treatments play a role too, most often in combination with pharmacological treatments or for patients who do not want pharmacological treatments. This is one subject in which practice varies enormously in different parts of the world. Readers who look at the text closely will notice considerable geographical variation in treatment polices, and it is a tribute to our authors that they have kept this to a minimum in this combined chapter. Introduction Opioid dependence is a chronic disorder characterized by relapse, increased mortality, significant medical morbidity, psychiatric sequelae and impaired social function in the individual. Accompanying these detrimental effects to the dependent individual are costs to their families and society secondary to impaired social and occupational functioning and increased criminal behavior or violence. In recent years there have been considerable gains in our understanding of the neurobiology of opioid dependence and in the development of new pharmacological and behavioral treatments for opioid dependence. Epidemiology In 1999 there were estimated to be 1 million chronic users of heroin in the United States, or 0.4% (4 per 1000) of the population (Rhodes et al ., 2000).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".