Possibility of screening for mild cognitive impairment via an eye tracking-based cognitive scale
Bibliographic record
Abstract
Introduction: The Montreal Cognitive Assessment (MoCA) is widely used as a screening test for mild cognitive impairment (MCI). However, the MoCA takes approximately 15 min to administer and evaluate by skilled examiners, such as medical professionals. This study assessed whether an eye tracking-based cognitive scale using virtual reality (VR) was accurate and efficient to screen for MCI. Methods: This study included 143 patients. The Virtual Reality-Based Cognitive Function Examination (VR-E) was used with all participants to evaluate their memory, judgment, spatial cognition, calculation, and language function. Results: Significant differences were observed in all cognitive domains of memory, judgment, spatial cognition, calculation, and language function between the Alzheimer's disease (AD), MCI, and older healthy control (HC) groups. The area under the curve value of the VR-E score for the HC and MCI groups was 0.857, and that for the AD and MCI groups was 0.870. The correlation coefficient between the MMSE and VR-E scores was 0.566 (p < 0.001), and that between the Japanese version of the MoCA (MoCA-J) and VR-E scores was 0.648 (p < 0.001), which indicated a moderate correlation in both comparisons. Conclusion: The VR-E had the same diagnostic performance results as the MoCA-J, thus the VR-E has potential for use in screening patients for MCI.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".