Rational choice of cholinesterase inhibitor for the treatment of Alzheimer's disease in Canada: a comparative economic analysis
Bibliographic record
Abstract
BACKGROUND: Cholinesterase inhibitors, such as galantamine, donepezil and rivastigmine are approved for symptomatic treatment of Alzheimer's Disease (AD) in Canada. In making choices amongst these drugs, one should consider their clinical merits and their economic implications. METHODS: Each drug's short-term efficacy was estimated based on independent Cochrane reviews of the clinical trials. Long-term clinical and economic outcomes were estimated using the Assessment of Health Economics in Alzheimer's Disease (AHEAD) model. RESULTS: While all treatments reduced the need for full-time care, only galantamine and donepezil 10 mg reduced the overall management costs of AD patients. The somewhat greater cognitive effect provided over six months by galantamine leads to the longest estimated delay before full-time care is required and, consequently to lower overall costs, with savings estimated at between 323 dollars and 4,246 dollars. CONCLUSION: Although there is uncertainty in estimated results, the best information currently available suggests that the first choice for treatment of AD should be galantamine. These results should be interpreted with caution, however, as results are not based on direct comparisons among the drugs and the differences emerging from meta-analyses of the trials are relatively small.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".