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Record W4408020996 · doi:10.18621/eurj.1618895

Cognitive dysfunction after ischemic stroke

2025· article· en· W4408020996 on OpenAlexaboutno aff
Derya Özdoğru, Miray Erdem

Bibliographic record

VenueThe European Research Journal · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Ischemic strokeCognitionCardiologyInternal medicinePhysical medicine and rehabilitationIschemiaPsychiatry

Abstract

fetched live from OpenAlex

Objectives: One of the most important and common consequences of ischemic stroke is the cognitive impairment observed after stroke. This study aimed to investigate the role of strategic stroke among post-stroke cognitive impairment using the Montreal Cognitive Assessment Scale (MOCA). Methods: This study was planned as a prospective cross-sectional study. Patients admitted between 3 and 12 months after stroke were included in the study. Patients who had a stroke at least 3 months ago, who had not been admitted for a year after the stroke, and who gave consent to participate in the study were considered as inclusion criteria. Results: This study included 45 (44.1%) females and 57 (55.9%) males. When evaluated in terms of comorbidity, the frequency of hyperlipidemia was found to be significantly higher in the cognitively impaired group (46.5% vs. 25.4%, P=0.027). Thyroid stimulating hormone (TSH) levels were found to be lower in the Cognitive Impairment Group (0.93 μIU/mL vs. 1.03 μIU/mL, P=0.021). Considering the finding rates and significance level of lesion sites between the groups, the strategic infarction rate was found to be significantly higher in the cognitively impaired group (62.8% vs. 33.9%, P=0.004). In cognitive tests, the cognitive impairment group showed significantly lower performance in all areas (P<0.05). Conclusions: It should be kept in mind that the MOCA scale can be a good evaluation scale in detecting patients with cognitive impairment after ischemic stroke.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.086
GPT teacher head0.367
Teacher spread0.280 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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