Montreal Cognitive Assessment as a screening tool
Bibliographic record
Abstract
BACKGROUND: We evaluated Montreal Cognitive Assessment (MoCA) performance in a veteran traumatic brain injury (TBI) population, considering performance validity test (PVT) and symptom validity test (SVT) data, and explored associations of MoCA performance with neuropsychological test performance and self-reported distress. METHODS: Of 198 consecutively referred veterans to a Veterans Administration TBI/Polytrauma Clinic, 117 were included in the final sample. The MoCA was administered as part of the evaluation. Commonly used measures of neuropsychological functioning and performance and symptom validity were also administered to aid in diagnosis. RESULTS: s < 0.05). Failure of both the SVT and at least 1 PVT yielded the lowest MoCA scores. Self-reported distress (both posttraumatic stress disorder symptoms and neurobehavioral cognitive symptoms) was also related to MoCA performance. CONCLUSIONS: Performance on the MoCA is influenced by task engagement and symptom validity. Causal inferences about neurologic and neurocognitive impairment, particularly in the context of mild TBI, wherein the natural course of recovery is well known, should therefore be made cautiously when such inferences are based heavily on MoCA scores. Neuropsychologists are well versed in the assessment of performance and symptom validity and thus may be well suited to explore the influences of abnormal performances on cognitive screening.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".