The Utility of Mobile Visuomotor Assessment for Neuropsychological Evaluation in Older Adults
Bibliographic record
Abstract
Performance in complex visuomotor tasks, where guiding visual information doesn't align spatially with the required motor output, relies on the brain's ability to integrate somatosensory information for an appropriate motor response. Performance on such "cognitive-motor integration" tasks is affected in Alzheimer’s disease. We investigate the relationship between a traditional neuropsychological test battery and a tablet-based visuomotor skill performance tasks. Older adults ranging from healthy to early Alzheimer’s disease completed the neuropsychological test battery, three tablet-based tasks and a series of tasks on the KINARM. We observed that 5 of our 6 CMI outcome measures were predictive of four tests from the WMS-IV battery, once variability for sex and age were accounted for; with one outcome variable significantly correlated between the two technologies. Our findings suggest that our multi-domain remotely deployable mobile task (BrDI) may be a good first step assessment tool in order to flag at-risk individuals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".