Tracking cognition with the T‐MoCA in a racially/ethnically diverse older adult cohort
Bibliographic record
Abstract
Introduction: We investigated the utility of the Telephone-Montreal Cognitive Assessment (T-MoCA) to track cognition in a diverse sample from the Einstein Aging Study. Methods: Telephone and in-person MoCA data, collected annually, were used to evaluate longitudinal cognitive performance. Joint models of T-MoCA and in-person MoCA compared changes, variance, and test-retest reliability measured by intraclass correlation coefficient by racial/ethnic group. Results: There were no significant differences in baseline performance or longitudinal changes across three study waves for both MoCA formats. T-MoCA performance improved over waves 1-3 but declined afterward. Test-retest reliability was lower for the T-MoCA than for the in-person MoCA. In comparison with non-Hispanic Whites, non-Hispanic Blacks and Hispanics performed worse at baseline on both MoCA formats and showed lower correlations between T-MoCA and in-person versions. Conclusions: The T-MoCA provides valuable information on cognitive change, despite racial/ethnic disparities and practice effects. We discuss implications for health disparity populations. Highlights: We assessed the comparability of Telephone-Montreal Cognitive Assessment (T-MoCA) and in-person MoCA for tracking cognition.Changes within 3 years in T-MoCA were similar to that for the in-person MoCA.T-MoCA is subject to practice effects and shows difference in performance by race/ethnicity.Test-retest reliability of T-MoCA is lower than that for in-person MoCA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".