Cost-Effectiveness: Cholinesterase Inhibitors and Memantine in Vascular Dementia
Bibliographic record
Abstract
BACKGROUND: Several randomized controlled trials of cholinesterase inhibitors and memantine in mild to moderate vascular dementia have demonstrated the efficacy of these treatments. However, given these drugs incur considerable cost, the economic argument for their use is less clear. OBJECTIVE: To determine the incremental cost-effectiveness of cholinesterase inhibitors and memantine for mild to moderate vascular dementia. DESIGN: A decision analysis model using a 24-28 week time horizon was developed. Outcomes of cholinesterase inhibitors and memantine and probabilities of adverse events were extracted from a systematic review. Costs of adverse events, medications, and physician visits were obtained from local estimates. Robustness was tested with probabilistic sensitivity analysis using a Monte Carlo simulation. INTERVENTIONS: Donepezil 5 mg daily, donepezil 10 mg daily, galantamine 16-24 mg daily, rivastigmine flexible dosing up to 6 mg twice daily, or memantine 10 mg twice daily versus standard care. MAIN OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) expressed as cost per unit decrease in the Alzheimer's Disease Assessment Scale-cognitive (ADAS-cog) subscale. RESULTS: Donepezil 10 mg daily was found to be the most cost-effective treatment with an ICER of $400.64 (95%CI, $281.10-$596.35) per unit decline in the ADAS-cog subscale. All other treatments were dominated by donepezil 10 mg, that is, more costly and less effective. CONCLUSION: From a societal perspective, treatment with cholinesterase inhibitors or memantine was more effective but also more costly than standard care for mild to moderate vascular dementia. The donepezil 10 mg strategy was the most cost-effective and also dominated the other alternatives.
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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 | 0.008 | 0.038 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".