Validity and reliability of a virtual reality system as an assessment tool for cognitive impairment based on the six cognitive domains
Bibliographic record
Abstract
The prevalence of neurocognitive disorders, including dementia is increasing in ageing populations globally. Conventional pen-and-paper neuropsychological assessments like the Montreal Cognitive Assessment (MoCA) are limited by their inability to correlate clinical cognitive scores with real-world functional performance. Efficacious, more ecologically valid and less operator-dependent assessment tools are needed to identify at-risk persons for early intervention. Technology-based tools like virtual reality (VR) are increasingly applied in healthcare, such as a novel “Cognitive Assessment using VIrtual REality” (CAVIRE-2) software which has been developed to assess the six domains of cognition automatically in 10 min. The study aimed to validate the CAVIRE-2 as a tool based on a matrix of scores and time to complete the 13 VR scenarios to discriminate persons who are cognitively healthy from those with MCI. Multi-ethnic Asian adults aged 55–84 years were recruited at a public primary care clinic in Singapore. Both CAVIRE-2 and MoCA were administered to each participant independently. 280 participants completed the study, of which 244 were found to be cognitively normal and 36 were cognitively impaired by MoCA. CAVIRE-2 showed moderate concurrent and convergent validity with MoCA and MMSE respectively. CAVIRE-2 demonstrated good test–retest reliability with Intraclass Correlation Coefficient of 0.89 (95% CI = 0.85–0.92, p < 0.001), and good internal consistency with Cronbach’s alpha = 0.87. CAVIRE-2 displayed good discriminative ability with area under curve (AUC) of 0.88 (95% CI = 0.81–0.95, p < 0.001), and an optimal cut-off score of < 1850 (88.9% sensitivity, 70.5% specificity, Youden’s = 0.59). CAVIRE-2 is potentially a valid and reliable assessment tool comparable to MoCA, which can distinguish cognitive status.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".