The Montreal Cognitive Assessment: Normative Data from a Large, Population-Based Sample of Cognitive Healthy Older Adults in Norway—The HUNT Study
Bibliographic record
Abstract
BACKGROUND: Several studies have found that normative scores on the Montreal Cognitive Assessment Scale (MoCA) vary depending on the person's education and age. The evidence for different normative scores between sexes is poor. OBJECTIVE: The main aim of the study was to determine normative scores on the MoCA for Norwegian older adults stratified by educational level, age, and sex. In addition, we aimed to explore sex differences in greater detail. METHODS: From two population-based studies in Norway, we included 4,780 people age 70 years and older. People with a diagnosis of dementia or mild cognitive impairment, a history of stroke, and depression were excluded. Trained health personnel tested the participants with the MoCA. RESULTS: The mean MoCA score varied between 22 and 27 and was highest among women 70-74 years with education >13 years and lowest among men age 85 and older with education ≤10 years. Education, age, and sex were significant predictors of MoCA scores. CONCLUSION: In the present study of cognitively healthy Norwegian adults 70 years and older, we found that the normative score on the MoCA varied between 22 and 27 depending on a person's education, age, and sex. We suggest that normative scores should be determined taking these three variables into consideration.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".