The Suitability of the Montreal Cognitive Assessment as a Screening Tool to Identify People with Dysfunction in Occupational Performance after Mild Stroke
Bibliographic record
Abstract
Introduction: Cognitive deficits are an important determinant for experiencing limitations in occupational performance after stroke. It is important to recognize these deficits, and their impact on daily activities, at an early stage so people can get the support they need. The non-challenging environment of a hospital hampers this recognition, making it difficult for occupational therapists to select which patients should be assessed extensively, and which not. This study aims to explore the Montreal Cognitive Assessment as a screening tool by investigating its relationship with occupational performance in patients with mild stroke. Method: Twenty-nine people with mild stroke were recruited for this cross-sectional study. Scores on the Montreal Cognitive Assessment and Assessment of Motor and Process Skills were compared and correlations were calculated. Findings: The Montreal Cognitive Assessment cut-off did not identify those who might experience problems in daily functioning after mild stroke. A moderate correlation was found between scores on the Montreal Cognitive Assessment and the Assessment of Motor and Process Skills process scores. Conclusion: The Montreal Cognitive Assessment cannot be used as a screening tool to identify problems in occupational performance after mild stroke.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".