A virtual reality cognitive screening tool based on the six cognitive domains
Bibliographic record
Abstract
Abstract INTRODUCTION Early detection of cognitive impairment enables interventions to slow cognitive decline. Existing neuropsychological paper‐and‐pencil tests may not adequately assess cognition in real‐life environments. A fully‐immersive and automated virtual reality (VR) system—Cognitive Assessment using VIrtual REality (CAVIRE)—was developed to assess all six cognitive domains. This case–control study aims to evaluate the ability of CAVIRE to differentiate cognitively‐healthy individuals from those with cognitive impairment. METHODS One hundred nine Asian individuals 65–84 years of age were recruited at a primary care setting in Singapore. Based on the Montreal Cognitive Assessment (MoCA), participants were grouped as either Cognitively Healthy (MoCA ≥26, n = 60) or Cognitively Impaired (MoCA <26, n = 49). Subsequently, all participants completed the CAVIRE assessment. RESULTS Cognitively‐healthy participants achieved higher VR scores and required shorter completion time across all six cognitive domains (all p’s < 0.005). Receiver‐operating characteristic curve analysis showed area under the curve of 0.7267. DISCUSSION The results demonstrated the potential of CAVIRE as a cognitive screening tool in primary care. Highlights CAVIRE is a virtual reality (VR) system that assesses the six cognitive domains. CAVIRE can distinguish healthy individuals from individuals with cognitive impairment. It has potential as a cognitive screening tool for older people in primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".