Treatment Preference for Alzheimer’s Disease: A Multicriteria Decision Analysis with Caregivers, Neurologists, and Payors
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a chronic neurodegenerative disorder associated with a high burden of illness. New therapies under development include agents that target amyloid-beta (Aβ), a key component in AD pathogenesis. Understanding the decision-making process for new AD drugs would help determine if such therapies should be adopted by society. Multicriteria decision analysis (MCDA) was applied to three key stakeholder groups to assess treatment alternatives for AD based on a multitude of decision trade-offs covering main components of care. METHODS: AD caregivers (n = 117), neurologists (n = 90), and payors (n = 90) from the USA received an online survey. The decision problem was broken down into four decision criterion and 12 subcriteria for two treatment scenarios: an Aβ-targeted therapy vs. the standard of care (SOC). Respondents were asked to indicate how much they preferred one option over another on a scale from 1 (equal preference) to 9 (high preference) based on each criterion and subcriterion. The decision criteria and subcriteria were weighted and presented as partial utility scores (pUS), with higher scores suggesting an increased preference for that decision-making component. RESULTS: Caregivers and payors applied the highest value to need for intervention (mean pUS = 0.303 and 0.259) and clinical outcomes (mean pUS = 0.286 and 0.377). In contrast, neurologists placed the highest value on clinical outcomes and types of benefits (mean pUS = 0.436 and 0.248). When decision subcriteria were examined, efficacy (mean pUS = 0.115, 0.219, and 0.166) and the type of patient benefits (mean pUS = 0.135, 0.178, and 0.126) were among the most valued by caregivers, neurologists, and payors. CONCLUSION: All groups placed the highest value on drug efficacy and types of benefit derived by patients. In contrast, cost implications were among the least important aspects in their decision-making.
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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 | 0.085 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".