New scoring methodology improves the sensitivity of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) in clinical trials
Bibliographic record
Abstract
INTRODUCTION: As currently used, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) has low sensitivity for measuring Alzheimer's disease progression in clinical trials. A major reason behind the low sensitivity is its sub-optimal scoring methodology, which can be improved to obtain better sensitivity. METHODS: Using item response theory, we developed a new scoring methodology (ADAS-CogIRT) for the ADAS-Cog, which addresses several major limitations of the current scoring methodology. The sensitivity of the ADAS-CogIRT methodology was evaluated using clinical trial simulations as well as a negative clinical trial, which had shown an evidence of a treatment effect. RESULTS: The ADAS-Cog was found to measure impairment in three cognitive domains of memory, language, and praxis. The ADAS-CogIRT methodology required significantly fewer patients and shorter trial durations as compared to the current scoring methodology when both were evaluated in simulated clinical trials. When validated on data from a real clinical trial, the ADAS-CogIRT methodology had higher sensitivity than the current scoring methodology in detecting the treatment effect. CONCLUSIONS: The proposed scoring methodology significantly improves the sensitivity of the ADAS-Cog in measuring progression of cognitive impairment in clinical trials focused in the mild-to-moderate Alzheimer's disease stage. This provides a boost to the efficiency of clinical trials requiring fewer patients and shorter durations for investigating disease-modifying treatments.
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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 | 0.266 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".