Can the general public use vignettes to discriminate between Alzheimer’s disease health states?
Bibliographic record
Abstract
Abstract Background Valid estimates of health-related quality-of-life (HRQoL) are often difficult to obtain from persons with Alzheimer’s disease (AD) and family caregiver proxies. To help assess whether the general public can serve as an alternate source of proxy HRQoL estimates in AD, we examined whether the general public can use vignettes to discriminate between AD health states. Methods We administered a telephone survey to randomly recruited participants from the general public who were aged 18 years or older. Interviewers read vignettes describing the mild, moderate, and severe AD health states to the participants, who answered the EQ-5D-5L and Quality of Life-Alzheimer’s Disease (QoL-AD) scales as if they had AD based on the vignette descriptions. Participants also answered the EQ-5D-5L for their current health states. We converted EQ-5D-5L responses into health utility scores using Canadian preference weights. We employed the Wilcoxon signed rank test to examine whether mean health utility scores and mean QoL-AD scores differed between health states. We used Pearson’s r to assess correlations between health utility and QoL-AD scores. Results Forty-eight participants (median age = 53 years; 25 female) completed the telephone interview; health utility and QoL-AD scores decreased as AD severity increased (p <0.0001). Mean health utility scores were 0.65 (mild), 0.51 (moderate), and 0.25 (severe). Mean QoL-AD scores were 26.7 (mild), 23.0 (moderate), and 17.4 (severe). The correlations between health utility and QoL-AD scores were moderate to strong (r ≥ 0.62). Conclusions Using the vignettes, the general public provided HRQoL estimates that discriminated between the three AD health states. This finding suggests the general public may be a promising source of proxy 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 | 0.023 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".