T‐MoCA: A valid phone screen for cognitive impairment in diverse community samples
Bibliographic record
Abstract
INTRODUCTION: There is an urgent need to validate telephone versions of widely used general cognitive measures, such as the Montreal Cognitive Assessment (T-MoCA), for remote assessments. METHODS: In the Einstein Aging Study, a diverse community cohort (n = 428; mean age = 78.1; 66% female; 54% non-White), equivalence testing was used to examine concordance between the T-MoCA and the corresponding in-person MoCA assessment. Receiver operating characteristic analyses examined the diagnostic ability to discriminate between mild cognitive impairment and normal cognition. Conversion methods from T-MoCA to the MoCA are presented. RESULTS: Education, race/ethnicity, gender, age, self-reported cognitive concerns, and telephone administration difficulties were associated with both modes of administration; however, when examining the difference between modalities, these factors were not significant. Sensitivity and specificity for the T-MoCA (using Youden's index optimal cut) were 72% and 59%, respectively. DISCUSSION: The T-MoCA demonstrated sufficient psychometric properties to be useful for screening of MCI, especially when clinic visits are not feasible.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".