Putting the Alzheimer's cognitive test to the test I: Traditional psychometric methods
Bibliographic record
Abstract
BACKGROUND: The Alzheimer's Disease Assessment Scale-Cognitive Behavior section (ADAS-Cog) is the most commonly used cognitive test in AD clinical trials. However, there are concerns about its use in early-stage disease. Herein we examine those concerns using traditional psychometric methods. METHODS: We analyzed ADAS-Cog data (n = 675) based on six psychometric properties: data completeness; scaling assumptions; targeting; reliability; validity; and responsiveness. RESULTS: At the scale-level, criteria tested for data completeness, scaling assumptions (item total correlations 0.33-0.59), targeting (no floor/ceiling effects), reliability (Cronbach's α = 0.74), and validity (correlation with MMSE = -0.70) were satisfied. Responsiveness (baseline to 12 months; n = 145) was moderate to high (effect size = -0.73). However, 8 of 11 ADAS-Cog components had substantial ceiling effects (range 32%-83%), and decreased responsiveness associated with low to moderate effect sizes (0.14-0.65). CONCLUSION: In our study, many patients with AD found large portions of the ADAS-Cog too easy. Future research should consider modifying the ADAS-Cog or developing a new test.
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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 | 0.061 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".