Regression-based normative scores for the Montreal Cognitive Assessment (MoCA) in an Asian population
Bibliographic record
Abstract
This study aimed to calculate stratified normative scores of the Montreal Cognitive Assessment (MoCA) in an adult population in Singapore, accounting for key demographic influences. Demographic data and MoCA scores of 1,103 healthy adults (aged 21 to 97) were obtained from a community health study conducted in central Singapore. Factors associated with MoCA scores were identified using multiple linear regression and β coefficients were used to estimate normative MoCA scores across strata. Model performance was assessed using five-fold cross-validation. Normative reference scores were calculated and stratified by age group, education level, and ethnicity to reflect typical MoCA performance across demographic groups. The final regression model had an adjusted R 2 of 0.284 ( p < 0.001), with age group (β = -0.325 to -2.312) and education level (β = 1.783 to 4.206) accounting for the majority of the explained variance (R 2 = 0.271). Ethnicity also remained a significant factor in the model, with lower scores observed among Malay (β = -1.248) and Indian (β =-0.795) participants compared to Chinese. Among the 64 demographic combinations of age group, education level and ethnicity, the lowest normative score (20.0) was derived for Malay individuals aged ≥ 75 years with no formal education. MoCA scores varied systematically with age, education level, and ethnicity in the study population. The resulting stratified reference scores provide clinicians and researchers a useful context for interpreting individual MoCA performance relative to demographically similar peers in Singapore’s adult population. However, these reference scores are not diagnostic thresholds and should be interpreted with caution until validated against clinically diagnosed cognitive impairment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".