Cognitive Task Virtualization for Alzheimer's Diagnosis Using Realistic VR Simulation
Bibliographic record
Abstract
Alzheimer's disease is a progressive neurodegenerative disease that worsens the patient's cognitive ability over time. This would affect the patient's cognitive ability, which would impact their daily quality of life. Through early diagnosis and testing, the patient can be diagnosed with the current condition of cognitive impairment. The existing method of assessing cognitive ability is through paper-based examinations, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). However, these examinations, which use paper and verbal answers, would provide a low ecological approach when it comes to testing the patient's cognitive performance. The availability of Virtual Reality technology, which enables the realistic simulation of real-life tasks, allows for more natural and practical testing of cognitive domains, presenting a potential for improvement in cognitive assessment. This research would adapt eight cognitive domains from the Montréal Cognitive Assessment and transform them into various simulation tasks to test the patient's ability. The simulation will be fully executed and operated using virtual reality devices to create immersive experiences. In the future, this VR simulation will be utilized to diagnose the severity of Alzheimer's Disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".