Normative data for the Montreal Cognitive Assessment (MoCA) and the Memory Index Score (MoCA‐MIS) in Brazil: Adjusting the nonlinear effects of education with fractional polynomials
Bibliographic record
Abstract
OBJECTIVE: To provide age-corrected and education-corrected norms for the Montreal Cognitive Assessment (MoCA) and the Memory Index Score (MoCA-MIS) in Brazil. METHODS: Community-dwelling outpatients were enrolled if they had no history of neurologic or psychiatric diseases and were not taking any drugs with effects on the central nervous system. Dementia has been excluded with the Functional Activities Questionnaire. The final sample consisted of 597 cognitively healthy Brazilians aged 50 to 90 years. To account for nonlinear relationships, we have used fractional polynomials that provide a flexible parameterization for continuous variables. RESULTS: According to the original proposed cutoff (≤25 points), 87% of our sample would be considered impaired. Even using a more conservative suggestion (≤22 points), 67% of our normative sample would be regarded as impaired. These data reinforce the need of adjusting cutoffs for schooling in populations with heterogeneous educational backgrounds. MoCA scores presented a nonlinear positive association with education tending to a plateau at higher levels (P < 0.001). On the other hand, MoCA-MIS scores presented a nonlinear negative relationship with age, with an accelerated pattern at higher age levels (P < 0.001). CONCLUSIONS: We presented normative data for the MoCA and the MoCA-MIS that will facilitate the use of the test in Brazil and, potentially, in other populations with substantial proportions of low-educated individuals. Moreover, we described a systematic approach for adjusting the effects of age and education using fractional polynomials and provided suggestions on how to account for the nonlinear relationship that is frequently encountered between demographic factors and measures of cognitive performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".