Montreal Cognitive Assessment in a 63- to 65-year-old Norwegian Cohort from the General Population: Data from the Akershus Cardiac Examination 1950 Study
Bibliographic record
Abstract
AIMS: To investigate Montreal Cognitive Assessment (MoCA) test scores in a cohort aged 63-65 years from a general population in relation to the proposed cut-off score of 26 for mild cognitive impairment (MCI) and to explore the impact of education. METHODS: MoCA scores were assessed in the Akershus Cardiac Examination 1950 Study, a cross-sectional cohort study of all men and women born in 1950 living in Akershus County, Norway. The participants were aged 63-65 at the time of data collection. RESULTS: MoCA scores were available in 3,413 participants, of which 47% had higher education (>12 years). The mean MoCA score was 25.3 (95% confidence interval [CI] 25.2-25.4), and 49% had a score below the suggested cut-off of 26 points. Those with higher education had significantly higher scores (mean 26.2, 95% CI 26.1-26.3 vs. 24.4, 95% CI 24.3-24.6, p < 0.001). CONCLUSIONS: Approximately 50% scored below the cut-off score of 26 points, suggesting that the cut-off score may have been set too high to distinguish normal cognitive function from MCI. Educational level had a significant impact on MoCA scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".