Virtual reality-based cognitive assessment tools for mild cognitive impairment screening: comparison with traditional paper-and-pencil-based cognitive assessment tools
Bibliographic record
Abstract
Background and Objectives: Dementia is becoming increasingly prevalent, highlighting the need for early detection of mild cognitive impairment (MCI), a risk state for dementia. Traditional cognitive assessments often require trained examiners and lack ecological validity. This study examined the diagnostic accuracy of a virtual reality (VR)-based cognitive assessment tool, VARABOM.D, by comparing it with three standard tests: Seoul Neuropsychological Screening Battery (SNSB), Montreal Cognitive Assessment (MoCA), and Mini-Mental State Examination (MMSE). Research Design and Methods: = 33) groups based on their Clinical Dementia Rating scores, completed the VARABOM.D program, in addition to SNSB, MoCA, and MMSE. Correlation analyses were performed on the test results, and the specificity, sensitivity, and area under the curve (AUC) of VARABOM.D were compared to those of the other assessments. To monitor for any adverse reactions to the VR environment, the Simulator Sickness Questionnaire (SSQ) was administered both before and after the VR sessions. Results: VARABOM.D scores showed significant positive associations with established cognitive assessments. Its AUC values were comparable to those of the MoCA, MMSE, and most SNSB subdomains except for attention, where VARABOM.D demonstrated greater discriminative ability. SSQ scores remained stable across pre- and post-VR sessions in both study groups, underscoring the VR platform's safety and feasibility. Discussion and Implications: VARABOM.D demonstrated accuracy comparable to traditional cognitive assessments and even outperformed the attention subdomain of SNSB. Additionally, no adverse reactions were observed in the normal or MCI groups, further emphasizing the safety and stability of VARABOM.D.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".