Intensive measurement of cognition to support early detection of cognitive change in individuals at risk of dementia
Bibliographic record
Abstract
Abstract Background Cognitive changes in nondemented older adults are best measured longitudinally and relative to individual baselines. Effect sizes of early changes in cognition are small with most traditional measures, and so there is a need for alternative measurement approaches that can improve statistical power to detect change. Remotely administered mobile assessments can address this need by permitting much more frequent repeated measurements, which then permits statistical delineation of early slopes of decline from performance variation associated with extraneous factors (e.g., mood, practice). MyCogHealth is an iOS/Android app for self‐administered cognitive assessments and customizable surveys. Inclusion of MyCogHealth in the Canadian Thumbs Up program (CTU) will permit evaluation of feasibility and utility of intensive measurement designs for capturing early cognitive changes in older adults identified to be at increased risk of dementia. Method The CTU Platform Trial Cohort will complete five intensive measurement “bursts” in three‐month intervals using MyCogHealth. Each burst involves brief self‐administered assessments completed twice daily across seven consecutive days. Each assessment includes the Symbol Match test (processing speed), Dot Memory (visual short‐term memory), a digital Trailmaking Test analogue, and surveys assessing state factors that can affect cognition. A pilot cohort (N=17; Mean age=74; Mean MoCA score=28) was followed for 12 months using this protocol to evaluate feasibility and psychometric integrity of the Symbol Match and Dot Memory tests in this design. Result In the pilot cohort Symbol Match correlated with Digit Span Forward and the Trailmaking Test (r=.51‐.66). Dot Memory correlated with Backward Digit Span (r=.49), Trailmaking Test (r=‐.59), object naming (r=.64), and visual episodic memory (r=.58). Retest reliability was .80 for Dot Memory and .87 for Symbol Search. Intraclass correlation was .77 for Symbol Search and .42 for Dot Memory. Participant retention was 100% and compliance with burst testing was 94%. Conclusion Preliminary results support the feasibility, validity, and reliability of self‐administered burst testing deployed longitudinally. Inclusion of MyCogHealth in CTU should be informative in capturing early slopes of decline in the study cohort. Such early detection of decline will inform more powerful, personalized trials where those at the highest predicted risk would be included and those at lower risk excluded.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".