A Predictive Microsimulation Model to Estimate the Clinical Relevance of Reducing Alcohol Consumption in Alcohol Dependence
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption is one of the most important factors for disease and disability in Europe. In clinical trials, nalmefene has resulted in a significant reduction in the number of heavy-drinking days (HDDs) per month and total alcohol consumption (TAC) among alcohol-dependent patients versus placebo. METHODS: A microsimulation model was developed to estimate alcohol-attributable diseases and injuries in patients with alcohol dependence and to explore the clinical relevance of reducing alcohol consumption. RESULTS: For all diseases and injuries considered, the number of events (inpatient episodes) increased with the number of HDDs and TAC per year. The model predicted that a reduction of 20 HDDs per year would result in 941 fewer alcohol-attributable events per 100,000 patients, while a reduction in intake of 3,000 g/year of pure alcohol (ethanol) would result in 1,325 fewer events per 100,000 patients. CONCLUSION: The potential gains of reducing consumption in alcohol-dependent patients were considerable.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".