Latent class analysis identifies functional decline with Amsterdam IADL in preclinical Alzheimer's disease
Bibliographic record
Abstract
Abstract Introduction Trials in Alzheimer's disease (AD) now include participants at the earliest stages to prevent further decline. However, the lack of tools sensitive to subtle functional changes in early‐stage AD hinders the development of new therapies as it is difficult to prove their clinical relevance. Methods We assessed functional changes over three years in 289 elderly memory complainers from the Investigation of Alzheimer's Predictors in subjective memory complainers cohort using the Amsterdam Instrumental‐Activities‐of‐Daily‐Living questionnaire (A‐IADL‐Q). Results No overall functional decline related to AD imaging markers was evidenced. However, five distinct classes of A‐IADL‐Q trajectories were identified. The largest class (212 [73.4%]) had stable A‐IADL‐Q scores over 3 years. A second group (23 [8.0%]) showed a persistent functional decline, higher amyloid load ( P = .0005), and lower education ( P = .0392). Discussion The A‐IADL‐Q identified a subtle functional decline in asymptomatic at‐risk AD individuals. This could have important implications in the field of early intervention in AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".