FLAME: A computerized neuropsychological composite for trials in early dementia
Bibliographic record
Abstract
INTRODUCTION: Sensitive neuropsychological tests are needed to improve power for clinical trials in early Alzheimer's disease (AD). METHODS: To develop a neuropsychological composite (FLAME - Factors of Longitudinal Attention, Memory and Executive Function), we assessed, 10,714 participants over the age of 50 from PROTECT with validated computerized assessments for 2 years. A factorial analysis was completed to identify the key cognitive factors in all participants, and further analyses examined sensitivity to change in people with stage 2/3 early Alzheimer's disease (AD) according to the US Food and Drug Administration (FDA) framework. RESULTS: The FLAME composite score (speed of attention, accuracy of attention, memory, and executive function) distinguished between normal cognition and stage 2/3 early AD at baseline, and was sensitive to cognitive and global/functional decline over 2 years, with the potential to improve power for clinical trials. DISCUSSION: FLAME is sensitive to change, providing a straightforward approach to reduce sample size for RCTs in early AD. CONCLUSION: FLAME is a useful computerized neuropsychology composite with utility for clinical trials focusing on cognition.
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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 | 0.074 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".