Development of Clinical Vignettes to Describe Alzheimer's Disease Health States: A Qualitative Study
Bibliographic record
Abstract
AIMS: To develop clinical descriptions (vignettes) of life with Alzheimer's disease (AD), we conducted focus groups of persons with AD (n = 14), family caregivers of persons with AD (n = 20), and clinicians who see persons with AD in their practices (n = 5). METHODS: Group participants read existing descriptions of AD and commented on the realism and comprehensibility of the descriptions. We used thematic framework analysis to code the comments into themes and develop three new vignettes to describe mild, moderate, and severe AD. RESULTS: Themes included the types of symptoms to mention in the new vignettes, plus the manner in which the vignettes should be written. Since the vignette descriptions were based on focus group participants' first-hand knowledge of AD, the descriptions can be said to demonstrate content validity. CONCLUSION: Members of the general public can read the vignettes and estimate their health-related quality-of-life (HRQoL) as if they had AD based on the vignette descriptions. This is especially important for economic evaluations of new AD medications, which require HRQoL to be assessed in a manner that persons with AD often find difficult to undertake. The vignettes will allow the general public to serve as a proxy and provide HRQoL estimates in place of persons with AD.
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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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".