Assessing the Equivalence of Telephone-MoCA and MoCA-22 in Healthy Adults
Bibliographic record
Abstract
Background and Purpose: The Telephone-Montreal Cognitive Assessment (T-MoCA) is a remote cognitive screening tool increasingly used in clinical and research contexts. However, its applicability in Korean populations remains underexplored. Methods: This study examined the equivalence between the T-MoCA and the face-to-face Korean MoCA-22 (K-MoCA-22) in a community-based sample of 113 cognitively normal adults aged 21-91 years. The sample was stratified into 2 age groups (<60 and ≥60 years) for subgroup analyses. All participants completed both the T-MoCA and K-MoCA-22 in a counterbalanced order, with a one-month interval between assessments. Results: Both T-MoCA and K-MoCA-22 scores were significantly associated with age and education, but not with sex in either age group. However, none of these variables significantly influenced the score differences between the 2 tests. Lin's concordance correlation coefficient indicated strong agreement between the 2 tests in the overall sample and in participants aged ≥60, with minimal systematic bias. Equivalence testing using the two one-sided tests procedure supported statistical equivalence in the total sample and the <60 group, but not in the ≥60 group. Subtest-level differences were observed in the ≥60 group, with higher repetition scores on the K-MoCA-22 and higher delayed recall scores on the T-MoCA, possibly reflecting absence of visual cues, reduced supervision, and lower anxiety during remote testing. Conclusions: These findings support the T-MoCA as an equivalent and reliable alternative to the K-MoCA-22 for remote cognitive screening in Korean adults. Nonetheless, age-related and modality-specific factors should be considered when interpreting scores, particularly in older individuals.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".