Remote and in‐clinic digital cognitive screening tools outperform the MoCA to distinguish cerebral amyloid status among cognitively healthy older adults
Bibliographic record
Abstract
Abstract INTRODUCTION We evaluated the accuracy of remote and in‐person digital tests to distinguish between older adults with and without AD pathological change and used the Montreal Cognitive Assessment (MoCA) as a comparison test. METHODS Participants were 69 cognitively normal older adults with known beta‐amyloid (Aβ) PET status. Participants completed smartphone‐based assessments 3×/day for 8 days, followed by TabCAT tasks, DCTclock™, and MoCA at an in‐person study visit. We calculated the area under the curve (AUC) to compare task accuracies to distinguish Aβ status. RESULTS Average performance on the episodic memory (Prices) smartphone task showed the highest accuracy (AUC = 0.77) to distinguish Aβ status. On in‐person measures, accuracy to distinguish Aβ status was greatest for the TabCAT Favorites task (AUC = 0.76), relative to the DCTclock TM (AUC = 0.73) and MoCA (AUC = 0.74). DISCUSSION Although further validation is needed, our results suggest that several digital assessments may be suitable for more widespread cognitive screening application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".