Impacts of education level on Montreal Cognitive Assessment and saccades in community residents from Western China
Bibliographic record
Abstract
OBJECTIVES: This cross-sectional study sought to evaluate the effectiveness of the Montreal Cognitive Assessment (MoCA) and saccade in discerning the cognitive function levels among community populations characterized by diverse educational backgrounds. METHODS: Data from 665 Western China individuals encompassed MoCA scores and saccade performance. The study examined how education level and age influenced these assessments and highlighted the contrasting abilities of these measures in detecting cognitive abnormalities. RESULTS: The saccade model revealed a consistent cognitive impairment prevalence (15.5%) compared to previous clinical data (9.7% to 23.3%), while MoCA exhibited variable rates (25.1% to 52.8%). Notably, saccades and MoCA significantly diverged in detecting cognitive dysfunction. Additionally, education level had a greater impact on MoCA (effect size: 0.272) compared to saccades (0.024) affecting all MoCA sub-items, with age exerting a smaller influence on MoCA (0.037) compared to saccades (0.056). CONCLUSION: Saccades are less susceptible to the influence of education level when compared to MoCA, making saccade a potentially more suitable cognitive screening tool for rural community populations. SIGNIFICANCE: This study represents a pioneering approach by employing saccade detection within community populations to distinguish cognitive function status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".