The Montreal Cognitive Assessment in Veteran Postacute Care: Implications of Cut Scores
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is often used for cognitive screening across health care settings, especially in rehabilitation centers, where assessment and treatment of cognitive function is considered key for successful multidisciplinary treatment. Although the original MoCA validation study suggested a cut score of <26 to identify cognitive impairment, recent studies have suggested that lower cut scores should be applied. OBJECTIVES: To examine the percentage of positive screens for cognitive impairment using the MoCA in a veteran postacute care (PAC) rehabilitation setting and to identify the most accurate MoCA cut score based on criterion neuropsychological measures. METHODS: We obtained data from 81 veterans with diverse medical diagnoses who had completed the MoCA during their admission to a PAC unit. A convenience subsample of 50 veterans had also completed four criterion neuropsychological measures. RESULTS: Depending on the cut score used, the percentage of individuals classified as impaired based on MoCA performance varied widely, ranging from 6.2% to 92.6%. When predicting performance using a more comprehensive battery of criterion neuropsychological tests, we identified <22 as the most accurate MoCA cut score to identify a clinically relevant level of impairment and <24 to identify milder cognitive impairment. CONCLUSIONS: Our findings suggest that a MoCA cut score of <26 carries a risk of misdiagnosis of cognitive impairment, and scores in the range of <22 to <24 are more reliable for identifying cognitive impairment.
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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 | 0.042 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".