Montreal Cognitive Assessment: Normative Data for Cognitively Healthy Swedish 80- to 94-Year-Olds
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is sensitive to cognitive impairment; however, it is also sensitive to demographic and socio-cultural factors. This necessitates reliable sub-population norms, but these are often lacking for older adults. OBJECTIVE: To present demographically adjusted regression-based MoCA norms for cognitively healthy Swedish older adults. METHODS: A pseudo-random sample of community-dwelling 80- to 94-year-olds, stratified by age and gender, was invited to the study. Initial telephone interviews and medical records searches (n = 218) were conducted to screen for cognitive impairment. N = 181 eligible participants were administered a protocol including the Swedish version of the MoCA and assessments of global cognition (Mini-Mental State Examination, MMSE) and depression (Patient Health Questionnaire-9, PHQ-9). Individuals scoring in the range of possible cognitive impairment on the MMSE or more than mild depression on the PHQ-9 were excluded (n = 23); three discontinued the test-session. RESULTS: Norms were derived from the remaining n = 158. They were evenly distributed by gender, on average 85 years old, and with a mean education of 11 years. MoCA scores were independently influenced by age and education, together explaining 17.2% of the total variance. Higher age and lower education were associated with lower performance and 46% performed below the original cut-off (< 26/30). CONCLUSION: The negative impact of increasing age on MoCA performance continues linearly into the nineties in normal aging. Demographic factors should be considered when interpreting MoCA performance and a tool for computing demographically corrected standard scores is provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".