Putting the Alzheimer's cognitive test to the test II: Rasch Measurement Theory
Bibliographic record
Abstract
BACKGROUND: The Alzheimer's Disease Assessment Scale-Cognitive Behavior section (ADAS-Cog) is the most widely used measure of cognitive performance in AD clinical trials. This key role has rightly brought its performance under increased scrutiny with recent research using traditional psychometric methods, questioning the ADAS-Cog's ability to adequately measure early-stage disease. However, given the limitations of traditional psychometric approaches, herein we use the more sophisticated Rasch Measurement Theory (RMT) methods to fully examine the strengths and weaknesses of the ADAS-Cog, and identify potential paths toward its improvement. METHODS: We analyzed AD Neuroimaging Initiative (ADNI) ADAS-Cog data (675 measurements across four time-points over 2 years) from the AD participants. RMT analysis was undertaken to examine three broad areas: adequacy of scale-to-sample targeting; degree to which, taken together, the ADAS-Cog items adequately perform as a measuring instrument; and how well the scale measured the subjects in the current sample. RESULTS: The 11 ADAS-Cog components mapped-out a measurement continuum, worked together adequately, and were stable across different time-points and samples. However, the scale did not prove to be a good match to the patient sample supporting previous research. RMT analysis also identified problematic "gaps" and "bunching" of the components across the continuum. CONCLUSION: Although the ADAS-Cog has the building blocks of a good measurement instrument, this sophisticated analysis confirms limitations with potentially serious implications for clinical trials. Importantly, and unlike traditional psychometric methods, our RMT analysis has provided important clues aimed at solving the measurement problems of the ADAS-Cog.
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 | 0.049 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".