Towards Practical Cognitive Assessment for Detection of Early Dementia: A 30-Minute Computerized Battery Discriminates as Well as Longer Testing
Bibliographic record
Abstract
Early detection of cognitive decline may lead to more effective treatment. Clinical cognitive assessment is essential for early detection, but must be brief with easily interpretable results. The present study defines and evaluates a 30-minute cognitive battery consisting of a subset of tests that comprise a longer computerized battery recently validated in detecting mild cognitive impairment (MCI). Participants were from three tertiary care memory clinics and an assisted living facility (final group: N=161) with consensus diagnoses of cognitively healthy, MCI, or mild dementia. A comprehensive NeuroTrax battery evaluated memory, executive function, visual spatial perception, verbal function, information processing speed, and motor skills. Validity of a single summary measure ('MCI Score') designed for dementia detection and built exclusively from tests of memory, executive function, and visual spatial perception was evaluated with receiver operating characteristic (ROC) analysis. Discriminant validity (area under the curve: AUC) was at least as large for the 6-parameter MCI Score as for a 20-parameter score necessitating administration of the entire battery. Further, the MCI Score had a larger AUC with reduced variance relative to its constituent parameters. AUC for distinguishing dementia was 0.886; AUC for distinguishing cognitively healthy was 0.823. Finally, the MCI Score discriminated among all three diagnostic groups (ANOVA; F[2,150]=52.54, p<0.001). Hence a reduced NeuroTrax battery (30 minutes) with MCI Score is a useful clinical tool for summarizing cognitive data relevant to early dementia detection.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".