ONLINE COGNITIVE MONITORING IN A SIMULATED ANTI-AMYLOID TRIAL: ADHERENCE AND COGNITIVE CHANGE
Bibliographic record
Abstract
Abstract Remote assessment of cognitive function is increasingly of interest for monitoring cognitive decline. Additional advantages of remote monitoring are more frequent assessments and the examination of change over time, which may yield more sensitive indicators of cognitive status. This concept was tested in DETECT-AD (Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer’s Disease), an ongoing simulated anti-amyloid trial using digital biomarkers as outcome measures. Adherence to testing, relationships to age, global cognition, and SUVR, and trajectories of change were analyzed in the SMART (Survey for Memory, Attention and Reaction Time) test, a brief measure of visual-grid memory and executive function (Trails A/B, Stroop Color-Word), self-administered online every four weeks. To date (02/28/2023), 46/100 have been enrolled with mean(SD): follow-up=31.0(7.9) weeks (no dropout); age=78(6.6); 63% women; MoCABaseline=25.5(2.3); PET SUVR=1.09 (0.20; range=0.850-2.07). SMART surveys (n=184) completed online used a desktop (26%), laptop (48%), tablet (20%), or smartphone (7%). A mean of 4(2.0; range=1-8) SMART surveys/participant were completed with 96% completed tests. Older age significantly correlated with longer overall completion times (p=0.02), Trails-B (p< 0.01) and Stroop (p=0.02) times, and more click counts on Trails-B (p=0.03). MoCA total score significantly correlated with shorter completion times on overall SMART (p=0.02), Trails-A (p< 0.01), and Trails-B (p=0.03). There were no relationships between SUVR and SMART. Total SMART completion time decreased over multiple administrations (p=0.03), driven by shorter Trails-B time (p=0.01). Monthly online screening of cognitive function in a clinical trial setting is well maintained yielding novel information over time.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".