Is the Cutoff of the MoCA too High? Longitudinal Data From Highly Educated Older Adults
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is widely used to evaluate cognitive decline in older individuals. Although, age and education-related norms have been published, the vast majority of diagnostic clinicians use the MoCA cutoff score to identify patients with cognitive impairment. AIM: To identify whether the MoCA cutoff is too stringent for cognitively normal older adults. METHODS: Twenty-seven participants aged 68 to 83 (mean = 75.07, standard deviation [SD] = 4.62), with high education level (mean = 17.14 years, SD = 3.21) underwent cognitive assessment once a year for 5 consecutive years. The cognitive assessment included MoCA; Rey Auditory Verbal Learning Test; Rey Osterrieth Complex Figure test; Wechsler Adult Intelligence Scale Information and Digit Span Subtest; Trail Making Test; Verbal Fluency Test; and Beck Depression Inventory questionnaire. Repeated measures analysis of variance (ANOVA) was used to analyze all standardized scores as well as MoCA standardized and raw scores across all years. RESULTS: < .05). From the second year and forward, the average MoCA total score was below the cutoff of 26/30. However, in substantial contrast, all other neuropsychological scores and the MoCA standardized scores were within the normal range and even above in all years. CONCLUSION: Our study demonstrates that the currently used MoCA cutoff is too high even for highly educated, cognitively normal older adults. Therefore, it is crucial to use the age- and education-related norms for the MoCA in order to avoid misdiagnosis of cognitive decline.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".