Pharmacotherapy of Alzheimer's disease: is there a need to redefine treatment success?
Bibliographic record
Abstract
The traditional aim of Alzheimer's disease treatment in clinical trials has been to improve cognitive abilities. It has become increasingly clear, however, that other aspects are important in assessing treatment responses. A group of 10 physicians recently gathered to review the current criteria for assessing treatment success in Alzheimer's disease. While cognition has been previously viewed as the primary measure of efficacy, areas such as functional abilities, behaviour, caregiver burden, quality of life and resource utilization all need to be comprehensively assessed to fully evaluate treatment effects in patients with Alzheimer's disease, as well as their impacts on caregivers and society. Postponing or slowing decline in any of these areas may represent an important benefit and should be considered as an outcome measure in clinical trials, clinical practice and decision-making about healthcare budgets. Accepted instruments are available for assessing outcomes in each aspect of Alzheimer's disease, but they need to be selected carefully to provide valid, meaningful data. Some of the most frequently used outcome measures in Alzheimer's disease are reviewed. Using expanded criteria for treatment success and clinically relevant outcome measures, data from currently available studies show that cholinesterase inhibitors produce clinically meaningful long-term benefits in multiple domains in patients with Alzheimer's disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".