Normative Data for the Montreal Cognitive Assessment in Middle-Aged and Elderly Quebec-French People
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Given that aging is associated with higher risk of cognitive decline and dementia, improving early detection of cognitive impairment has become a research and clinical priority. The Montreal Cognitive Assessment (MoCA) is a screening instrument used to assess different aspects of cognition. Despite its widespread use, norms adjusted to the sociodemographics of Quebec-French people are not yet available. Such norms are however important because performance on neuropsychological tests varies according to sociodemographic variables including age, sex, and education. As such, the present study aimed to establish normative data for the MoCA in middle-aged and elderly Quebec-French population. METHOD: For that purpose, 1,019 community-dwelling older adults aged between 41 and 98 were recruited. Participants from 12 recruiting sites completed the MoCA. Regression-based normative data were produced and cross-validated with a validation sample (n = 200). RESULTS: Regression analyses indicated that older age, lower education level, and male sex were associated with poorer MoCA scores. The best predictive model included age (p < .001), education (p < .001), sex (p < .001), and a quadratic term for education (education X education; p < .001). This model explained a significant amount of variance of the MoCA score (p < .001, R2 = 0.26). A regression equation to calculate Z scores is presented. CONCLUSIONS: This study provides normative data for the MoCA test in the middle-aged and elderly French-Quebec people. These data will facilitate more accurate detection and follow-up of the risk of cognitive impairment in this population, taking into account culture, age, education, and sex.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 it