Detecting Everyday Action Deficits in Alzheimer’s Disease Using a Nonimmersive Virtual Reality Kitchen
Bibliographic record
Abstract
Alzheimer's disease (AD) causes impairments affecting instrumental activities of daily living (IADL). Transdisciplinary research in neuropsychology and virtual reality has fostered the development of ecologically valid virtual tools for the assessment of IADL, using simulations of real life activities. Few studies have examined the benefits of this approach in AD patients. Our aim was to examine the utility of a non-immersive virtual coffee task (NI-VCT) for assessment of IADL in these patients. We focus on the assessment results obtained from a group of 24 AD patients on a task designed to assess their ability to prepare a virtual cup of coffee, using a virtual coffee machine. We compared performance on the virtual task to an identical daily living task involving the actual preparation of a cup of coffee, as well as to global cognitive, executive, and caregiver-reported IADL functioning. Relative to 32 comparable, healthy elderly (HE) controls, AD patients performed worse than HE controls on all tasks. Correlation analyses revealed that NI-VCT measures were related to all other neuropsychological measures. Moreover, regression analyses demonstrated that performance on the NI-VCT predicted actual task performance and caregiver-reported IADL functioning. Our results provide initial support for the utility of our virtual kitchen for assessment of IADL in AD patients.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".