Correlating Eye-Tracking Fixation Metrics and Neuropsychological Assessment after Ischemic Stroke
Bibliographic record
Abstract
Background and Objectives: Stroke survivors commonly experience cognitive deficits, which significantly impact their quality of life. Integrating modern technologies like eye tracking into cognitive assessments can provide objective and non-intrusive measurements. Materials and Methods: This study aimed to evaluate the cognitive and visual processing capabilities of stroke patients using eye-tracking metrics and psychological evaluations. A cohort of 84 ischemic stroke patients from the N-PEP-12 clinical study was selected for secondary analysis, based on the availability of eye-tracking data collected during a visual search task using an adapted Trail Making Test. Standardized cognitive assessments, including the Montreal Cognitive Assessment (MoCA) and digit span tasks, were also conducted. Results: Correlation analyses revealed some notable relationships between eye-tracking metrics and cognitive measures, such as a positive correlation between Symbol Search performance and the number of fixations. Anxiety levels were found to be positively correlated with first fixation duration, while longer first fixation durations were associated with poorer cognitive performance. However, most correlations were not statistically significant. Nonparametric ANOVA showed no significant differences in fixation metrics across the visits. Conclusions: These findings suggest a complex relationship between cognitive status, gaze fixation behavior, and psychological well-being in stroke patients. Further research with larger sample sizes and analysis of saccadic eye movements is needed to better understand these relationships and inform effective interventions for stroke rehabilitation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".