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Cognitive impairment in patients with atrial fibrillation and arterial hypertension

2022· article· en· W4223930611 on OpenAlexaboutno aff
A. Ya. Kovaleva, В. Л. Лукинов, Г. И. Лифшиц

Bibliographic record

VenuePatologiya krovoobrashcheniya i kardiokhirurgiya · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
FundersRussian Academy of SciencesSiberian Branch, Russian Academy of Sciences
KeywordsAtrial fibrillationMedicineCardiologyInternal medicineCognitionStroke (engine)Psychiatry

Abstract

fetched live from OpenAlex

Aim. To study the influence of atrial fibrillation on the severity of cognitive impairment in patients with arterial hypertension.Methods. The study included 25 patients with atrial fibrillation and arterial hypertension, the control group of 25 patients with arterial hypertension, but without cardiac arrhythmias. All patients underwent general clinical and instrumental examination of the cardiovascular system. The Montreal Cognitive Assessment test was used to assess memory and attention, the degree of mastering visual-constructive skills, abstract thinking and speech.Results. Cognitive functions in patients with atrial fibrillation were significantly worse than in patients in the control group (testing to assess indicators: 22.7 ± 3.2 and 25.6 ± 2.2 points, respectively, p < 0.001). Cognitive indicators such as memory, speech and abstract thinking are most severely affected in patients with arrhythmia.Conclusion. Atrial fibrillation creates conditions for the development of cognitive deficits. Cerebral hypoperfusion, the occurrence of "silent" cerebral infarctions and hypercoagulation are important pathogenetic factors of cognitive impairment in patients with atrial fibrillation. Received 29 July 2021. Revised 11 September 2021. Accepted 20 September 2021. Funding: The research was carried out within the state assignment of the Siberian Branch of the Russian Academy of Sciences (No. 121031300045-2). Conflict of interest: Authors declare no conflict of interest. Contribution of the authors: The authors contributed equally to this article.

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 score0.016
Threshold uncertainty score1.000

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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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