The Montreal Cognitive Assessment: Normative Data from a German-Speaking Cohort and Comparison with International Normative Samples
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Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is used to evaluate multiple cognitive domains in elderly individuals. However, it is influenced by demographic characteristics that have yet to be adequately considered. OBJECTIVE: The aim of our study was to investigate the effects of age, education, and sex on the MoCA total score and to provide demographically adjusted normative values for a German-speaking population. METHODS: Subjects were recruited from a registry of healthy volunteers. Cognitive health was defined using the Mini-Mental State Examination (score ≥27/30 points) and the Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (total score ≥85.9 points). Participants were assessed with the German version of the MoCA. Normative values were developed based on regression analysis. Covariates were chosen using the Predicted Residual Sums of Squares approach. RESULTS: The final sample consisted of 283 participants (155 women, 128 men; mean (SD) age = 73.8 (5.2) years; education = 13.6 (2.9) years). Thirty-one percent of participants scored below the original cut-off (<26/30 points). The MoCA total score was best predicted by a regression model with age, education, and sex as covariates. Older age, lower education, and male sex were associated with a lower MoCA total score (p < 0.001). CONCLUSION: We developed a formula to provide demographically adjusted standard scores for the MoCA in a German-speaking population. A comparison with other MoCA normative studies revealed considerable differences with respect to selection of volunteers and methods used to establish normative data.
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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 | 0.000 |
| 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 | 0.001 |
| 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