Clinical relevance of nalmefene versus placebo in alcohol treatment: Reduction in mortality risk
Bibliographic record
Abstract
Reduction of long-term mortality risk, an important clinical outcome for people in alcohol dependence treatment, can rarely be established in randomized controlled trials (RCTs). We calculated the reduction in all-cause mortality risk using data from short-term (6 and 12 months) double-blind RCTs comparing as-needed nalmefene treatment to placebo, and mortality risks from meta-analyses on all-cause-mortality risk by reduction of drinking in people with alcohol dependence. A reduction in drinking in the RCTs was defined by shifts in drinking risk levels established by the European Medicines Agency. Results showed that the reduction of drinking in the nalmefene group was associated with a reduction in mortality risk by 8% (95% CI: 2%, 13%) when compared to the placebo group. Sensitivity analyses confirmed a significant effect. Thus comparing the difference between nalmefene and placebo in reduction in drinking levels with results on all-cause mortality risk from meta-analyses indicated a clinically relevant reduction in mortality risk. Given the high mortality risk of people with alcohol dependence, abstinence or a reduction in drinking have been shown to reduce mortality risk and should be considered treatment goals.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".