Screening and facilitating further assessment for cognitive impairment after stroke: application of a shortened Montreal Cognitive Assessment (miniMoCA)
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to examine the performance of a shortened version of the MoCA (miniMoCA), as a clinical cognitive impairment screening tool in stroke rehabilitation patients. METHODS: Cognitive status was assessed using the MoCA and Cognistat in 72 patients. Agreement between the tests was assessed using the Kappa statistic. The sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of a miniMoCA to a MoCA score <26 was also examined. RESULTS: A significant level of agreement was found between the MoCA and miniMoCA to the Cognistat in classifying patients by level of cognitive function. The miniMoCA showed a sensitivity of 93% and specificity of 92% (PPV 98%, NPV 75%) to abnormal MoCA scores (<26). CONCLUSIONS: This study extends the utility of the miniMoCA as an optimal brief screening tool for cognitive impairment in stroke patients. Further research is needed to determine the validity of the miniMoCA against a neuropsychological test. IMPLICATIONS FOR REHABILITATION: Although the Montreal Cognitive Assessment (MoCA) is a recommended tool to screen for cognitive impairment in stroke patients, its lengthy administration can lead to inconsistent screening of patients for post-stroke cognitive function. In the current work, a shortened version of the MoCA (miniMoCA) was administered in a sample of stoke inpatients, utilizing only five of the eight original subtests. The proposed miniMoCA was found to streamline the administration of this screen test, while maintaining a heightened level of sensitivity for accurately identifying which patients do not require a more in-depth cognitive assessment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".