The Montreal Cognitive Assessment: Normative Data from a Large, Population-Based Sample of Healthy Adults in China
Bibliographic record
Abstract
Abstract Background The Montreal Cognitive Assessment (MoCA) is a valuable tool for detecting cognitive impairment, but its accuracy is significantly influenced by demographic and socio-cultural factors. Consequently, the development of appropriate normative values becomes particularly crucial in ensuring its reliable use and interpretation. Objective Generate MoCA normative values based on demographics for healthy chinese adults. Methods The assessment conducted in this study utilizes the MoCA scale, specifically employing the Mandarin-8.1 version (Chinese Mandarin version). Based on the geographical distribution of administrative regions in mainland china, this study recruited a total of 3,097 healthy individuals aged over 20 years. Drawing on insights from prior normative studies, we performed multiple linear regression analysis, incorporating age, gender, and education level as predictor variables, to examine their associations with the total score and sub-cognitive domains of MoCA. Subsequently, we established normative values and cut-off values stratified by age and education level. Results The participants in this study (n=3,097) exhibit a balanced gender distribution, with an average age of 54.46 years (SD 14.38) and an average education period of 9.49 years (SD 4.61). The study population demonstrates an average MoCA score of 23.25 points (SD 4.82). The findings from the multiple linear regression analysis indicate that the total score of MoCA is influenced by age and education level, collectively accounting for 46.8% of the total variance. Higher age and lower education level are correlated with lower MoCA total scores. Conclusion This study offers normative MoCA values specific to the Chinese population. Furthermore, the research findings indicate that a score of 26 may not represent the most optimal cut-off value. When assessing MoCA scores, it is imperative to comprehensively account for the participants’ age and educational background.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".