The Cost-Effectiveness of the Integration of Nalmefene within the UK Healthcare System Treatment Pathway for Alcohol Dependence
Bibliographic record
Abstract
AIMS: To assess the cost-effectiveness of integrating nalmefene within the treatment pathway for alcohol dependence recommended by the National Institute for Health and Care Excellence in the UK. METHODS: A Markov model, taking a UK NHS perspective, followed a cohort with alcohol dependence and high/very high drinking risk levels (HVHDRLs), who do not require immediate detoxification and who continue at HVHDRLs after initial assessment, for 5 years. Costs and quality-adjusted life years (QALYs) from treatment with nalmefene plus psychosocial support versus psychosocial support alone were modelled. The consequent incidence of alcohol-attributable harmful events and disease progression, with the possibility of requiring other options or recurrent treatment, were captured. RESULTS: Nalmefene plus psychosocial support dominated psychosocial support alone, with lower costs and increased QALYs after 5 years. Savings are driven by the higher response to nalmefene, and the subsequent lower cost accumulation for alternatives. CONCLUSIONS: Nalmefene represents a highly cost-effective treatment option in this population. The analysis shows that integrating nalmefene within the current UK clinical treatment pathway for alcohol dependence could reduce the economic burden on the NHS by limiting harmful events and disease progression.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".