Prevalence of Heavy Alcohol Use Among People Receiving Methadone Following Change to Methadose
Bibliographic record
Abstract
BACKGROUND: A recent switch in methadone formulation from methadone (1 mg/mL) to Methadose (10 mg/mL) in British Columbia (BC), Canada, was associated with increased reports of opioid withdrawal and increases in illicit opioid use. Impacts on other forms of drug use have not been assessed. Since alcohol use is common among people receiving Medication-Assisted Treatment (MAT), we assessed if switch was associated with increased prevalence of heavy alcohol use. METHODS: Drawing on data from two open prospective cohort studies of people who inject drugs in Vancouver, BC, generalized estimating equations (GEE) model examined relationship between methadone formulation change and heavy alcohol use, defined by National Institute for Alcohol Abuse and Alcoholism (NIAAA). A sub-analysis examined relationship with heavier drinking defined as at least eight drinks per day on average in last six months. RESULTS: Between June 2013 and May 2015, a total of 787 participants on methadone were eligible for the present analysis, of which 123 (15.6%) reported heavy drinking at least once in last six months. In an unadjusted GEE model, Methadose use was not significantly associated with an increased likelihood of heavy drinking [Odds Ratio (OR) = 1.03; 95% Confidence interval (CI) = 0.87-1.21]. Methadose use was not significantly associated with an increased likelihood of drinking at least eight drinks daily on average (OR = 1.09, 95% CI = 0.72-1.65). CONCLUSIONS: Despite reported changes in opioid use patterns coinciding with the change, there appeared to be no effect of the methadone formulation change on heavy drinking in this setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".