A-021 Normative Data on Montreal Cognitive Assessment Total Scores in Healthy Controls
Bibliographic record
Abstract
Abstract The Montreal Cognitive Assessment (MoCA) is considered to be a suitable, sensitive, and specific cognitive screening tool for detecting mild cognitive impairment. Research has reported variable cutoff scores for the MoCA based upon geographical location. The aim of the present study is to provide normative data in a sample of cognitively healthy adults. Data was collected through the National Alzheimer’s Coordinating Center (NACC). A population of healthy adults (N = 3610) was examined (66% female, 78% Caucasian, 16% African American, 6% Other). MoCA normative data were derived from age and education, which were found to be weakly but significantly associated with age (r = −.203, p = .000) and more strongly correlated with education (r = .402, p = .000). Total scores (M = 26.25, SD = 2.75) were at the suggested cutoff for impairment (< 26). Based on an ANOVA, age had a significant effect on MoCA scores (F (6, 3603) = 25.30, p < .001). A second ANOVA revealed that education also had a significant effect on MoCA scores (F (2, 3582) = 290.56, p < .001). Individuals with higher levels of education obtained higher MoCA scores. Performance was also found to decrease slightly with age. Therefore, clinicians should use caution when applying the recommended cutoff scores.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".