Development of a straightforward and sensitive scale for MCI and early AD clinical trials
Bibliographic record
Abstract
BACKGROUND: Although the Clinical Dementia Rating Scale-Sum of Boxes score (CDR-SB) is a widely accepted and commonly used global scale, validated clinical endpoints of cognitive changes are unavailable in the predementia stages of Alzheimer's disease (AD), and a new clinical assessment with reliability and sensitivity is needed in the mild cognitive impairment (MCI) population. METHODS: Using Alzheimer's Disease Neuroimaging Initiative (ADNI)-1/GO data, signal-to-noise ratios (SNRs) were calculated to quantify the sensitivity of a measure for detecting disease progression and hypothetical treatment effects. All possible combinations of selected sensitive measures were assessed for developing composite scores. The analyses were performed in the MCI population and subpopulations enriched by apolipoprotein E4 (APOE ε4), hippocampal volume, and cerebrospinal fluid β-amyloid. RESULTS: The best composite score was "Word Recall + Delayed Word Recall + Orientation + CDR-SB + FAQ", more sensitive than 13-item Alzheimer's Disease Assessment Scale-cognitive subscale or CDR-SB. CONCLUSION: The proposed composite score derived from the existing clinical endpoints demonstrated higher sensitivity in the MCI population and is easy to implement and standardize across studies.
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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 | 0.251 | 0.341 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".