Memory-Guided Saccades in Subacute and Chronic Stroke: Secondary Data Analysis of the N-PEP-12 Clinical Study
Bibliographic record
Abstract
BACKGROUND: Ischemic stroke (IS) often leads to cognitive and motor impairments. This study aimed to investigate whether Memory-Guided Saccade Tasks (MGSTs) could be used to assess cognitive function in stroke patients. METHODS: A secondary data analysis was conducted on 62 individuals with supratentorial IS. Eye-tracking metrics from MGST were correlated with established neuropsychological assessments, including the Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HADS). RESULTS: Age correlated negatively with memory-guided saccade (MGS) accuracy (ρ = -0.274) and positively with late errors (ρ = 0.327). Higher Montreal Cognitive Assessment (MoCA) scores were associated with faster corrective saccades (ρ = 0.259). Increased anxiety (HADS-A) and depression (HADS-D) levels correlated with higher early error rates (ρ = 0.325 and ρ = 0.311, respectively). The Color Trails Test and Digit Span test performance also correlated with various MGS parameters. CONCLUSIONS: While some correlations were found between cognitive measures and eye-tracking metrics, further research is needed to validate MGST as a tool for cognitive assessment in a more homogenous stroke population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".