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Record W4281724701 · doi:10.1007/s13760-022-01967-4

Montreal Cognitive Assessment of cognitive dysfunction after basal ganglia stroke

2022· article· en· W4281724701 on OpenAlexaboutno aff
Baoye Ye, Dingqun Wei, Lin Pan

Bibliographic record

VenueActa Neurologica Belgica · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersFujian Provincial Department of Science and Technology
KeywordsMontreal Cognitive AssessmentBasal gangliaStroke (engine)Executive dysfunctionCognitionMedicinePsychologyInternal medicineAudiologyPsychiatryCognitive impairmentNeuropsychologyCentral nervous system

Abstract

fetched live from OpenAlex

OBJECTIVE: The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive dysfunction after basal ganglia stroke, and factors affecting total MoCA score were examined. METHODS: Data were retrospectively analyzed for 30 patients with basal ganglia intracerebral hemorrhage or basal ganglia cerebral infarction, who were admitted to The Second Affiliated Hospital of Fujian Traditional Medical University (Fujian, China) from January 2017 to March 2020. Cognitive impairment was assessed using the MoCA, and potential correlations were explored between clinicodemographic characteristics (sex, age, stroke location and etiology) and MoCA dimensions or total MoCA score. RESULTS: Univariate linear regression showed that the total MoCA score was significantly associated with sex, age, executive function, naming, attention, abstract generalization ability, memory ability, and visuospatial orientation. However, multivariate linear regression identified only executive function, naming, attention, memory ability, and visuospatial orientation as significantly associated with the total MoCA score. CONCLUSIONS: We showed that the MoCA test can be used for patients with basal ganglia stroke. The total MoCA score of basal ganglia stroke was significantly associated with impairments in executive function, naming, attention, memory ability, and visuospatial orientation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

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