Entering the new era of cognitive scoring? Eye-tracking assessment in neurodegenerative disorders
Bibliographic record
Abstract
Background: The inci dence of dementia and cognitive deterioration is on the rise. Therefore, objective, fast and repetitive cognitive scoring methodology to screen the population and guide the diagnostic process is needed. Eye-tracking provides gaze patterns metrics based on the pupil size and the point of gaze assessment. Methods: The study evaluated 60 patients with medical anamnesis, Montreal Cognitive Assessment – MoCA test, Geriatric Depression Scale – GDS, and eye-tracking protocol. The novel object recognition test consisted of 30 seconds observation of the set of three images, followed by a 90-second pause, and a repeated 30-second observation of the set of three images with the change of one of them. The comparison was made between the metrics of three subgroups, which were created based on MoCA score and named as controls: ≥26, mild cognitive impairment: 21–25, and dementia: <20. Results: For the novel object recognition task, a control group compared to a dementia group was more interested in the new object during a free observation (repeated measure ANOVA, p = 0.03). Moreover, during the observation of the second set of images, the pupil dilation as a result of a memory recall is more prominent in the control group (t-test, p = 0.009). Conclusion: Eye-tracking is a potentially useful tool for objective assessment of patients’ cognitive status. Further studies are needed to evaluate norms across different ages and cut-off points.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".