A Fully-Immersive and Automated Virtual Reality System to Assess the Six Domains of Cognition: Protocol for a Feasibility Study
Bibliographic record
Abstract
Introduction: Dementia is increasing in prevalence in aging populations. Current questionnaire-based cognitive assessments may not comprehensively assess cognitive function and real-time task-performance. Virtual reality (VR) technology has been used in cognitive assessments but existing systems have limited scope in evaluating all cognitive domains. We have developed a novel, fully-immersive VR system (CAVIRE: Cognitive Assessment by VIrtual REality), which incorporates automated audio-visual instructions. An automated scoring matrix to assess the six cognitive domains—perceptual-motor function, executive function, complex attention, social cognition, learning and memory, and language—is embedded in the CAVIRE system. Aims: The primary aim is to evaluate the feasibility of using the CAVIRE system to assess cognitive function of participants across different age groups from 35 to 84 years old. The secondary aims are to determine the CAVIRE performance-indices (completion time and scores) of the participants and to assess their acceptability toward the use of CAVIRE as a modality for cognitive assessment. Methods: One hundred and seventy-five participants will be assessed by CAVIRE at a primary care clinic in Singapore. They will be first assessed using questionnaires: Montreal Cognitive Assessment (MoCA), Abbreviated Mental Test (AMT), Mini-Mental State Examination (MMSE), Basic Activities of Daily Living (BADLs), Instrumental Activities of Daily Living (IADLs). Those aged 65–84 years will be grouped into cognitively intact ( n = 50, MoCA score ≥ 26) and cognitively impaired ( n = 50, MoCA < 26). The CAVIRE performance-indices of cognitively healthy younger participants aged 35–64 years ( n = 75) will serve as benchmark references. CAVIRE auto-computes the participant's performance-indices in 13 different segments. The tasks include domestic chores, memory, shopping, and social interactions. The proportion of participants who complete the entire VR assessment in each age group will be evaluated as feasibility indicators. The CAVIRE performance-indices will be compared across the different age groups. Feedback on the acceptability of the CAVIRE system will be collated and compared among the participants across the age groups. Significance: CAVIRE is designed to assess the six domains of cognitive function using VR. The results of this feasibility study will provide insights for the implementation of the CAVIRE system as an alternative modality of cognitive assessment in the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".