Test Accuracy of the Montreal Cognitive Assessment in Screening for Early Poststroke Neurocognitive Disorder
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We determined the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) for poststroke neurocognitive disorder defined according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria in a prospective observational study. METHODS: Consecutive participants able to complete a cognitive test battery and MoCA 3 months poststroke were included. The reference standard of neurocognitive disorder was defined as a score of ≥1.5 SD below the normative mean in ≥1 cognitive domain on the cognitive test battery. RESULTS: Among 521 participants (43.6% women; mean age/SD, 71.5/12.0 years; mean education/SD, 12.4/3.8 years), the area under the receiver operating characteristic curve of MoCA for neurocognitive disorder was 0.80 (95% CI, 0.76-0.84). Using the standard MoCA cutoff <26, sensitivity was 0.71 (0.69-0.79) with specificity of 0.73 (0.66-0.76). MoCA cutoff of <27 gave higher sensitivity (0.82 [0.77-0.85]) at the expense of specificity (0.60 [0.53-0.66]). DISCUSSION: MoCA has reasonable accuracy for poststroke neurocognitive disorder diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02650531.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".