Influence of education level on domain‐specific MOCA performance in the MarkVCID cohort
Bibliographic record
Abstract
BACKGROUND: Education level is a well-recognized co-factor for cognitive performance and a potential confounder in the application of cognitive evaluations in diverse populations. The Montreal Cognitive Assessment (MoCA) is a widely utilized screening tool for Mild Cognitive Impairment (MCI) composed of 6 domains of cognition: Executive Function (EFC), Attention and Concentration (AC), Language (LANG), Visuospatial (VIS), Memory (MEM), and Orientation (ORIEN). Education is currently accounted for globally by adding 1 point to the total MoCA score. This study considers the impact of education level on a domain-specific scoring of the MoCA. METHOD: We utilized longitudinal subject level data from one site within the MarkVCID Consortium (UCSF) composed of 578 subjects (59.5% female; 82.9% white), mean age 71.5 (+/- 8.7). Mean educational level was 15.3 (+/-4.7) years. Within the cohort, 37 subjects lacked educational level and were omitted (final n = 541). Multivariate linear regression models were used to relate educational level with total and domain specific MoCA score performance at baseline while controlling for the influence of age, sex, and recognized MarkVCID biomarkers that impact cognitive performance including white matter hyperintensity (logWMH), fractional anisotropy (FA), free water (FW), and peak width of skeletonized mean diffusivity (PSMD). RESULTS: An age-, sex-adjusted model replicated a significant association of education level with MoCA total score (p<0.001, b = .336). Among the MoCA domains, EFC was most affected by education level (p<.001, b = .412), followed by AC (p<.001, b = .352). Other MoCA domains were not significantly associated with educational level. Several MoCA domains, namely MEM and ORIEN, showed an association with MarkVCID biomarkers including WMH and FW (p-values <.001). In regression models controlled for MarkVCID imaging biomarkers, educational level remained significantly associated with total and domain-specific MoCA performance. CONCLUSION: Education does not equally contribute to all cognitive domains assessed by the MoCA, mostly affecting EFC and AC. These findings suggest that considering education level in a domain-specific manner could provide a more accurate interpretation of the cognitive impairment.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".