Cognitive assessment of post-stroke patients with and without aphasia: The Hebrew version of the Cognitive Assessment for Stroke Patients (CASP) vs. the Montreal Cognitive Assessment (MoCA)
Bibliographic record
Abstract
Cognitive screening assessments for neurological deficits are critical to the initial assessment of post-stroke patients. However, most measures are not designed for post-stroke patients and in particular not for people with aphasia (PWA), because they rely on language functions. The Cognitive Assessment for Stroke Patients (CASP) is a screening test that can also be administered to PWA, and was recently adapted into Hebrew. The current study aimed to compare the performance of post-stroke patients on the Hebrew versions of the CASP and the Montreal Cognitive Assessment (MoCA). Forty medical records of post-stroke patients were retrospectively examined: Twenty participants without aphasia and 20 PWA. The data included demographics, total CASP and MoCA scores, and scores in specific cognitive domains. Correlations were found between total CASP and MoCA scores, for all participants as well as for each group separately. Comparisons between groups revealed significantly higher performance of the participants without aphasia on the MoCA, but not on the CASP. Clinically, these findings suggest that the Hebrew version of the CASP can be implemented as a formal cognitive screening test for post-stroke patients, including PWA. It can help identifying PWA's cognitive state and differentiate between language and cognitive impairments, hence, contributing in planning targeted treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".