Economic Analysis of Galantamine, a Cholinesterase Inhibitor, in the Treatment of Patients with Mild to Moderate Alzheimer’s Disease in The Netherlands
Bibliographic record
Abstract
BACKGROUND: The economic impact of dementia on the Dutch health and social services is substantial. OBJECTIVE: To predict the long-term economic impact of galantamine, a cholinesterase inhibitor, in the treatment of Dutch patients with mild to moderate Alzheimer's disease. METHOD: A pharmacoeconomic model was used to predict long-term outcomes. It has two components: an initial module based on clinical trials of galantamine and a subsequent module that predicts when a patient will deteriorate to a level where full time care (FTC) is needed. The analyses take a broad perspective that includes all formal (paid) care, not just those covered by the Dutch health care system. Direct cost estimates were based on resource use profiles of patients with Alzheimer's disease in the Netherlands. Key inputs were tested in sensitivity analyses. RESULTS: After 10.5 years all patients are predicted to require FTC. For every hundred patients starting treatment on galantamine at the mild to moderate stage, it is predicted that 18 person-years of FTC will be avoided (14.4 discounted) and about 5 quality-adjusted years of life will be gained (3.9 discounted). Net savings for those starting treatment with galantamine are estimated at NLG 3,050 (1,676 UDS). The cost of galantamine accounts for only about 5.0% of the total cost of care for treated Alzheimer's patients. The direction of these results remained unchanged when input values and assumptions were tested in sensitivity analyses. CONCLUSIONS: The cholinesterase inhibitor galantamine is expected to bring savings in the direct cost of caring for patients with Alzheimer's disease in the Netherlands.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".