Technical Review of Clinical Outcomes Assessments Across the Continuum of Alzheimer's Disease
Bibliographic record
Abstract
INTRODUCTION: Insight into the relationship between concepts that matter to the people affected by Alzheimer's disease (AD) and the clinical outcome assessments (COAs) commonly used in AD clinical studies is limited. Phases 1 and 2 of the What Matters Most (WMM) study series identified and quantitatively confirmed 42 treatment-related outcomes that are important to people affected by AD. METHODS: We compared WMM concepts rated as "very important" or higher to items included in COAs used commonly in AD studies. RESULTS: Twenty COAs designed to assess signs, symptoms, and impacts across the spectrum of AD were selected for review. Among these 20 COAs, only 5 reflected 12 or more WMM concepts [Integrated Alzheimer's Disease Rating Scale (iADRS), Alzheimer's Disease Cooperative Study-Activities of Daily Living Inventory (ADCS-ADL), Alzheimer's Disease Cooperative Study-Activities of Daily Living Inventory-Mild Cognitive Impairment (ADCS-ADL-MCI), Alzheimer's Disease Composite Scores (ADCOMS), and Clinical Dementia Rating; Clinical Dementia Rating-Sum of Boxes (CDR/CDR-SB)]. Multiple symptoms and impacts of AD identified as important and meaningful in the WMM studies map only indirectly at best to 7 of the 20 most widely used COAs. CONCLUSION: While many frequently used COAs in AD capture some concepts identified as important to AD populations and their care partners, overlap between any single measure and the concepts that matter to people affected by AD is limited. The highest singly matched COA reflects fewer than half (45%) of WMM concepts. Use of multiple COAs expands coverage of meaningful concepts. Future research should explore the content validity of AD COAs planned for AD trials based on further confirmation of the ecological validity of the WMM items. This research should inform development and use of core outcome sets that capture WMM items and selection or development of new companion tools to fully demonstrate clinically meaningful outcomes spanning WMM.
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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 | 0.115 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".