Use of pharmacotherapy for alcohol use disorder in Manitoba, Canada: A whole-population cohort study
Bibliographic record
Abstract
OBJECTIVE: Update the evidence on use of pharmacotherapy for alcohol use disorder in a Canadian population. METHODS: Using whole-population administrative data from Manitoba, Canada, we identified all residents age 12+ who were first diagnosed with alcohol use disorder between April 1, 1996 and March 31, 2015, and compared characteristics of those who filled a prescription for naltrexone, acamprosate or disulfiram at least once during that period to those who did not fill a prescription for an alcohol use disorder medication. RESULTS: Only 1.3% of individuals with alcohol use disorder received pharmacotherapy (62.3% of prescriptions were for naltrexone, 39.4% for acamprosate, 7.5% for disulfiram). Most prescriptions came from family physicians in urban alcohol use disorder (53.6%) and psychiatrists (22.3%). Individuals were more likely to fill a prescription for alcohol use disorder medication if they lived in an urban vs rural environment (OR 2.25; 95% CI 1.83-2.77) or had a mood/anxiety disorder diagnosis vs no diagnosis (OR 2.40, 95% CI 1.98-2.90) in the five years before being diagnosed with alcohol use disorder. CONCLUSION: Despite established evidence for the effectiveness of pharmacotherapy for alcohol use disorder, these medications continue to be profoundly underutilized in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".