Assessing What Matters to People Affected by Alzheimer’s Disease: A Quantitative Analysis
Bibliographic record
Abstract
INTRODUCTION: In this phase of the ongoing What Matters Most study series, designed to evaluate concepts that are meaningful to people affected by Alzheimer's disease (AD), we quantified the importance of symptoms, impacts, and outcomes of AD to people at risk for or with AD and care partners of people with AD. METHODS: We administered a web-based survey to individuals at risk for or with AD (Group 1: unimpaired cognition with evidence of AD pathology; Group 2: AD risk factors and subjective cognitive complaints/mild cognitive impairment; Group 3: mild AD) and to care partners of individuals with moderate AD (Group 4) or severe AD (Group 5). Respondents rated the importance of 42 symptoms, impacts, and outcomes on a scale ranging from 1 ("not at all important") to 5 ("extremely important"). RESULTS: Among the 274 respondents (70.4% female; 63.1% white), over half of patient respondents rated all 42 items as "very important" or "extremely important," while care partners rated fewer items as "very important" or "extremely important." Among the three patient groups, the minimum (maximum) mean importance rating for any item was 3.4 (4.6), indicating that all items were at least moderately to very important. Among care partners of people with moderate or severe AD, the minimum (maximum) mean importance rating was 2.1 (4.4), indicating that most items were rated as at least moderately important. Overall, taking medications correctly, not feeling down or depressed, and staying safe had the highest importance ratings among both patients and care partners, regardless of AD phase. CONCLUSION: Concepts of importance to individuals affected by AD go beyond the common understanding of "cognition" or "function" alone, reflecting a desire to maintain independence, overall physical and mental health, emotional well-being, and safety. Preservation of these attributes may be key to understanding whether interventions deliver clinically meaningful outcomes.
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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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".