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Record W2760956191 · doi:10.17116/jnevro20171176253-57

Cognitive impairment and compliance in chronic heart failure

2017· article· en· W2760956191 on OpenAlexaboutno aff
M. V. Shestakova, А. Ф. Василенко, Maria B. Karpova, Е. А. Григоричева, Н. В. Епанешникова, I. V. Kochetkov

Bibliographic record

VenueS S Korsakov Journal of Neurology and Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsHeart failureMedicineLeukoaraiosisEjection fractionCompliance (psychology)NeuropsychologyStroke (engine)CardiologyCognitionCognitive impairmentInternal medicinePsychologyDementiaPsychiatry

Abstract

fetched live from OpenAlex

AIM: To determine the factors of cognitive impairment and non-compliance in patients with chronic heart failure (CHF). MATERIAL AND METHODS: One hundred and fifty-seven patients with CHF and 32 patients without chronic heart failure (controls) were examined. Neuropsychological assessment using a 30-minutes protocol recommended by NINDS-Canadian Stroke Standards (NINDS CSS), magnetic resonance imaging, cognitive ERP and a test of compliance were used. RESULTS AND CONCLUSION: Approximately 62% of patients did not perform medical prescriptions. Non-compliance was associated with an increase in the severity of subcortical and perivenricular leukoaraiosis, a slowdown in decision-making processes (an increase in the P300 latency), worse performance on speech activity tests, optical/spatial and frontal dysfunctions and memory. Patients with non-compliance had frontal cognitive impairment (58%), memory impairment (40%) and mixed forms (21%). Cognitive impairment in patients with chronic heart failure was associated with the lower left ventricular ejection fraction and deterioration in indices of diastolic function.

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 score0.050
Threshold uncertainty score0.338

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 integrity0.0000.001
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 head0.025
GPT teacher head0.361
Teacher spread0.337 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueS S Korsakov Journal of Neurology and PsychiatrySame topicCardiac Health and Mental HealthFrench-language works237,207