A Hospital-based Managed Alcohol Program in a Canadian Setting
Bibliographic record
Abstract
OBJECTIVES: A managed alcohol program (MAP) is a harm reduction strategy that provides regularly, witnessed alcohol to individuals with a severe alcohol use disorder. Although community MAPs have positive outcomes, applicability to hospital settings is unknown. This study describes a hospital-based MAP, characterizes its participants, and evaluates outcomes. METHODS: A retrospective chart review of MAP participants was conducted at an academic hospital in Vancouver, Canada, between July 2016 and October 2017. Data included demographics, alcohol/substance use, alcohol withdrawal risk, and MAP indication. Outcomes after MAP initiation included the change in mean daily alcohol consumption and liver enzymes. RESULTS: Seventeen patients participated in 26 hospital admissions: 76% male, mean age of 54 years, daily consumption prehospitalization of a mean 14 alcohol standard drinks, 59% reported previous nonbeverage alcohol consumption, and 41% participated in a community MAP. Most participants were high risk for severe, complicated alcohol withdrawal and presented in moderate withdrawal. Continuation of community MAP was the most common indication for hospital-based MAP initiation (38%), followed by a history of leaving hospital against medical advice (35%) and hospital illicit alcohol use (15%). Hospital-based MAP resulted in a mean of 5 fewer alcohol standard drinks daily compared with preadmission ( P = 0.002; 95% confidence interval, 2-8) and improvement in liver enzymes, with few adverse events. CONCLUSIONS: Participation in a hospital-based MAP may be an effective safe approach to reduce harms for some individuals with severe alcohol use disorder. Further study is needed to understand who benefits most from hospital-MAP and potential benefits/harms following hospital discharge.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".