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Record W4400888302 · doi:10.1080/23279095.2024.2376032

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)

2024· article· en· W4400888302 on OpenAlexaboutno aff
Naama Rosenheck, Asnat Bar-Haim Erez, Michal Biran

Bibliographic record

VenueApplied Neuropsychology Adult · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentAphasiaStroke (engine)CognitionCASPCognitive Assessment SystemMedicinePsychologyCognitive impairmentPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.305
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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