Questioning the Meaning of a Change on the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog): Noncomparable Scores and Item-Specific Effects Over Time
Bibliographic record
Abstract
to track change over time using ordinary mean comparisons. The Alzheimer's Disease Assessment Scale-cognitive (ADAS-Cog) and its subscale scores are often used to monitor the progression of Alzheimer's disease, but longitudinal invariance has not been formally evaluated. A configural invariance model was used to evaluate ADAS-Cog data as a three correlated factors structure for two visits over 6 months, and four visits over 2 years (baseline, 6, 12, and 24 months) among 341 participants with Alzheimer's disease. We also attempted to model ADAS-Cog subscales individually, and furthermore added item-specific latent variables. Neither the three-correlated factors ADAS-Cog model, nor its subscales viewed unidimensionally, achieved longitudinal configural invariance under a traditional modeling approach. No subscale achieved scalar invariance when considered unidimensional across 6 months or 2 years of assessment. In models accounting for item-specific effects, configural and metric invariance were achieved for language and memory subscales. Although some of the ADAS-Cog individual items were reliable, comparisons of summed ADAS-Cog scores and subscale scores over time may not be meaningful due to a lack of longitudinal invariance.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".