Normative study of the Taiwanese version of the Montreal Cognitive Assessment (MoCA) in community-dwelling individuals in Taiwan
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) may not be appropriately interpreted in Taiwan because of the lack of large-scale normative data. Moreover, examinees' demographic characteristics may influence their MoCA scores. However, previous studies have not adequately adjusted for these effects. This study aimed to use regression-based methods to establish demographically adjusted MoCA norms. METHODS: Participants were recruited from six hospitals and neighboring communities from all geographic areas of Taiwan. Multiple regression analyses were conducted to quantify the effects of age, education, and sex on MoCA total and domain scores, resulting in correction equations and adjusted cutoff scores. RESULTS: A total of 2310 cognitively healthy participants were included in the analysis. Age and education significantly affected the total and all domain scores. Sex affected naming, language, and abstract thinking domain scores. Correction equations and corresponding cutoffs were proposed for MoCA total and domain scores to support more precise clinical interpretations. CONCLUSION: This study provides regression-adjusted norms for the MoCA, improving its accuracy and clinical utility in Taiwan. An adjusted total MoCA score of 23 points is recommended as the cutoff for identifying potential cognitive impairment, with domain-specific cutoffs further supporting individualized interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".