Differential Validity of K-MoCA-22 Compared to K-MoCA-30 and K-MMSE for Screening MCI and Dementia
Bibliographic record
Abstract
Background and Purpose: Since the onset of the coronavirus disease 2019 pandemic, the Telephone-Montreal Cognitive Assessment (T-MoCA) has gained popularity as a remote cognitive screening tool. T-MoCA includes items from the original MoCA (MoCA-30), excluding those requiring visual stimuli, resulting in a maximum score of 22 points. This study aimed to assess whether the T-MoCA items (MoCA-22) demonstrate comparable discriminatory power to MoCA-30 and Mini-Mental State Examination (MMSE) in screening for mild cognitive impairment (MCI) and dementia. Methods: Participants included 233 cognitively normal (CN) individuals, 175 with MCI, and 166 with dementia. All completed the Korean-MoCA-30 (K-MoCA-30) and Korean-MMSE (K-MMSE), with the Korean-MoCA-22 (K-MoCA-22) scores derived from the K-MoCA-30 responses. A receiver operating characteristic (ROC) curve analysis was conducted. Results: K-MoCA-22 showed a strong correlation with K-MoCA-30 and a moderate correlation with K-MMSE. Scores decreased progressively from CN to MCI and dementia, with significant differences between groups, consistent with K-MoCA-30 and K-MMSE. The study also explored modified K-MoCA-22 index scores across 5 cognitive domains. ROC curve analysis revealed that the area under the curve (AUC) for K-MoCA-22 was significantly smaller than that for K-MoCA-30 in distinguishing both MCI and dementia from CN. However, no significant difference in AUC was found between K-MoCA-22 and K-MMSE, indicating similar discriminatory power. Additionally, the discriminability of K-MoCA-22 varied by education level. Conclusions: These results indicate that K-MoCA-22, although slightly less effective than K-MoCA-30, still shows good to excellent discriminatory power and is comparable to K-MMSE in screening for MCI and dementia.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".