C-33 Abnormal MoCA Scores in a Clinic-Referred Sample
Bibliographic record
Abstract
Abstract Objective To examine Montreal Cognitive Assessment (MoCA) performance and frequency of low scores among veterans with primary diagnoses of cognitive disorder, psychiatric disorder, or no disorder. Method A clinic-referred sample of veterans (n = 214; Mage = 66.1, SD = 15; Medu = 13.3, SD = 2.7) diagnosed with mild cognitive impairment (MCI; n = 97), dementia (n = 47), depression (n = 18), PTSD (n = 22), or no cognitive or psychiatric disorder (n = 30) were included. All participants were administered the MoCA as part of a larger battery of tests. Analysis of covariance (ANCOVA), controlling for age and education, was conducted (Bonferroni correction applied) to compare diagnostic groups on MoCA uncorrected total score. Results Across groups, mean MoCA scores were significantly different using ANCOVA, F(4, 207) = 31.5, p < .001. As expected, those with no diagnosis (M = 24.7, SD = 2.1) or psychiatric disorders (PTSD M = 24.4, SD = 4.1; Depression M = 23.9, SD = 3) scored higher than those with cognitive disorder (MCI M = 21.7, SD = 3.1; Dementia M = 17.4, SD = 4.1), p < .001. While both psychiatric groups scored higher than those with dementia (p < .001), the depression group did not significantly differ from those with MCI (p = .11). Examination of scores across all groups revealed a majority of participants scored below the recommended cutoff of < 26. Specifically, 100% of dementia cases, 89.7% of MCI cases, 63.3% of no diagnosis cases, 50% of PTSD cases, and 72.2% of depression cases scored < 26. Conclusion Abnormal MoCA scores are common, even in the absence of cognitive impairment. Individuals with PTSD or depression are likely to score below the publisher's recommend cutoff. While this may reflect cognitive symptoms of psychiatric conditions, it may also reflect normative limitations as identified in past studies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".