Cost-effectiveness analysis of deep brain stimulation for the treatment of alcohol use disorder and alcoholic liver disease
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is a major public health concern and cause of mortality and morbidity. Alcohol-associated liver disease (ALD) is a debilitating complication of AUD, mitigated by abstinence from alcohol use. Deep brain stimulation (DBS) is emerging as a potential treatment for AUD. However, its cost-effectiveness compared to the standard medical treatment is unclear. OBJECTIVE: To estimate the cost-effectiveness of DBS compared to medical management for patients with AUD and ALD. METHODS: We utilized a decision analytic model based on published literature to conduct a cost-effectiveness analysis of costs and health outcomes for DBS and medical management in patients with AUD and ALD. We also carried out a threshold analysis to determine the probability of success necessary for DBS to be cost-effective. Costs were measured in 2024 US dollars and effectiveness in quality-adjusted life years (QALYs). We used a time horizon of 1-2 years and adopted a societal perspective. RESULTS: Our results show that for AUD patients in general, DBS is not cost-effective at any DBS success rate. However, for advanced ALD patients, defined as fibrosis stage 3 or beyond DBS becomes cost-effective. For these patients, DBS is cost-effective over a two-year period at a $100,000 willingness-to-pay threshold at DBS success rates greater than 53 %. For advanced decompensated ALD patients, DBS is cost-effective over a one-year period at DBS success rate greater than 35 %. CONCLUSION: Should it prove efficacious, DBS may be cost-effective for patients with AUD and ALD. Thus, future randomized controlled trials to evaluate its efficacy are warranted.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".