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Record W2188355146 · doi:10.18192/uojm.v5i2.1308

The Importance of Longitudinal Neurocognitive Assessments in Heart Failure Patients Receiving a Left Ventricular Assist Device

2015· article· en· W2188355146 on OpenAlexaffvenueabout
Sneha Raju, Vanessa Rojas-Luengas

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNeurocognitiveMedicineHeart failureVentricular assist deviceInternal medicineIncidence (geometry)CardiologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT:Heart failure (HF) is a major global health concern that has continued to increase in incidence and prevalence, becoming a global epidemic. In Canada alone, there are 500,000 HF patients, with 50,000 new cases each year. Often, HF patients reach severe end stage HF (ESHF) and require a heart transplant or a left ventricular assist device (LVAD). Previous studies have shown that as the heart begins to fail, ESHF patients develop a global cognitive impairment (CI) that accompanies the reduction in blood pressure (BP) and cardiac output (CO). Several mechanisms have been attributed to the CI observed in ESHF patients. Cerebral hypoperfusion, due to a large decrease in CO, appears to be the most supported explanation. Although several studies to date have explored cognitive functioning after the treatment of HF, there is limited literature investigating the cognitive outcome in ESHF patients following LVAD implantation. Moreover, studies that examined the effect of LVAD implantation on cognition did not compare patient outcomes to pre-LVAD baseline levels. Taking into consideration the increasing number of EDHF patients in need of LVAD implantation each year, it is imperative to determine the effect of this intervention on CI in order to better inform LVAD patients and create effective rehabilitation programs for LVAD recipients.RÉSUMÉ:L’insuffisance cardiaque (IC) est une préoccupation majeure de santé mondiale qui continue d’augmenter en incidence et en prévalence, devenant une épidémie mondiale. Au Canada seulement, 500 000 patients souffrent d’IC, avec 50 000 nouveaux cas chaque année. Souvent, les patients avec IC atteignent une phase terminale grave (ICT) et nécessitent une transplantation cardiaque ou un dispositif d’assistance ventriculaire gauche (DAVG). Des études antérieures ont démontré que lorsque le cœur est en insuffisance, les patients développent une déficience cognitive globale (DCG), accompagnant la réduction de la tension artérielle et du débit cardiaque. Plusieurs mécanismes ont été attribués à la DCG observée chez les patients en ICT. L’hypoperfusion cérébrale, en raison d’une diminution importante du débit cardiaque, semble être l’explication la plus soutenue. Bien que plusieurs études à ce jour ont exploré le fonctionnement cognitif suivant le traitement de l’IC, il existe une littérature limitée enquêtant le résultat cognitif chez les patients en ICT suivant l’implantation d’un DAVG. Par ailleurs, les études qui ont examiné l’effet de l’implantation de DAVG sur la cognition ne comparaient pas les résultats des patients avec leurs niveaux de base pré-DAVG. Prenant en considération le nombre croissant de patients en ICT en besoin d’implantation de DAVG chaque année, il est impératif de déterminer l’effet de cette intervention sur la DCG afin de mieux informer les patients avec DAVG et créer des programmes efficaces de réhabilitation pour les bénéficiaires de DAVG.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.280
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2015
Admission routes3
Has abstractyes

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