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PREDICTORS OF COGNITIVE DETERIORATION DURING THE FIRST YEAR AFTER ISCHEMIC NON-LACUNAR STROKES IN PATIENTS WITH ATRIAL FIBRILLATION

2025· article· en· W4411694079 on OpenAlexaboutno aff
M. Yu. Delva, V.V. Zayets, Наталія Ігорівна Чекаліна, I. I. Delva

Bibliographic record

VenueEastern Ukrainian Medical Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAtrial fibrillationCardiologyInternal medicineMedicineLacunar strokeIschemic strokeCognitive impairmentFibrillationIschemiaDisease

Abstract

fetched live from OpenAlex

Introduction. Epidemiological studies revealed that AF may be an independent predictor of cognitive impairment, including post-stroke patients. AF patients had statistically significant deterioration of cognitive functioning during the first year after ischemic strokes. However, the factors associated with cognitive deterioration during the post-stroke period in AF patients have not yet been identified. Objective: to study the factors associated with cognitive deterioration during the first year after ischemic non-lacunar strokes in patients with AF. Materials. In the final analysis, we included 65 patients with AF who had an ischemic non-lacunar stroke within the last 6 months. The cognitive assessment consisted of a Mini-Mental State Examination, Montreal Cognitive Assessment, Clock Drawing Test, and Frontal Assessment Battery. “Cognitive deterioration” was defined as ≥ 1 point decrease by any of the cognitive scales at the 12-month visit compared to the initial visit score. As predictors of cognitive deterioration, we studied socio-demographic, psycho-emotional, comorbid, neurological, functional, neuroimaging factors, lipid profile, and transthoracic echocardiographic parameters. Results. According to all of the used cognitive scales, the same factors were independent predictors of cognitive deterioration during the first year after ischemic non-lacunar strokes in patients with AF – severe leukoaraiosis (Fazekas scale score >3), reduced left ventricle ejection fraction and increased left atrium size. The optimal thresholds of left ventricle ejection fraction values for predicting cognitive deterioration, depending on the cognitive scale, were within the interval of 41–48%, whereas the optimal threshold of left atrium size for predicting cognitive deterioration, regardless of the scale used, was the same – 41 mm. Conclusions. Independent predictors of cognitive deterioration in AF patients during the first year after ischemic non-lacunar strokes are leukoaraiosis, low left ventricle ejection fraction, and high left atrium size.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.269

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 science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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