RESEARCH A chance to stop and brea
Bibliographic record
Abstract
Full list of author information is available at the end of the articlecan be fatal if untreated [1]. Abstinence-based therapies, a first-line treatment choice in many countries, have been shown to successfully treat patients who have stable hous-ing, family support, and personal motivation (e.g., readi-ness for change) [2]. However, the marginalization that often accompanies long-term opioid dependence [3] is of patients remaining abstinent after one year [4]. Opioid substitution-based therapies, mainly oral methadone main-tenance treatment (MMT), are considered the most effect-ive options for the treatment of opioid dependence [2]. Although MMT is effective for many patients [5,6], it is estimated that 15–25 percent of the most severely af-fected individuals with opioid dependence are not reached or retained by MMT [7,8]. These individuals do not stay in MMT for very long, or they continue to use illicit opioids while in treatment [9,10]. Studies in Europe and Canada have demonstrated that supervised, medically
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.136 | 0.059 |
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".