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Record W3024119213 · doi:10.1016/j.hroo.2020.05.002

Atrial remodeling and atrial fibrillation in acquired forms of cardiovascular disease

2020· article· en· W3024119213 on OpenAlexafffund
Hailey J. Jansen, Loryn J. Bohne, Anne M. Gillis, Robert A. Rose

Bibliographic record

VenueHeart Rhythm O2 · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of CalgaryLibin Cardiovascular Institute of Alberta
FundersCanadian Institutes of Health ResearchKillam TrustsHeart and Stroke Foundation of Canada
KeywordsAtrial fibrillationHeart failureInternal medicineCardiologyMedicineDiabetes mellitusMuscle hypertrophyDiseaseHeart diseaseFibrosisVentricular remodelingLeft ventricular hypertrophyEndocrinologyBlood pressure

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is prevalent in common conditions and acquired forms of heart disease, including diabetes mellitus (DM), hypertension, cardiac hypertrophy, and heart failure. AF is also prevalent in aging. Although acquired heart disease is common in aging individuals, age is also an independent risk factor for AF. Importantly, not all individuals age at the same rate. Rather, individuals of the same chronological age can vary in health status from fit to frail. Frailty can be quantified using a frailty index, which can be used to assess heterogeneity in individuals of the same chronological age. AF is thought to occur in association with electrical remodeling due to changes in ion channel expression or function as well as structural remodeling due to fibrosis, myocyte hypertrophy, or adiposity. These forms of remodeling can lead to triggered activity and electrical re-entry, which are fundamental mechanisms of AF initiation and maintenance. Nevertheless, the underlying determinants of electrical and structural remodeling are distinct in different conditions and disease states. In this focused review, we consider the factors leading to atrial electrical and structural remodeling in human patients and animal models of acquired cardiovascular disease or associated risk factors. Our goal is to identify similarities and differences in the cellular and molecular bases for atrial electrical and structural remodeling in conditions including DM, hypertension, hypertrophy, heart failure, aging, and frailty.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.063
GPT teacher head0.303
Teacher spread0.240 · 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

Citations65
Published2020
Admission routes2
Has abstractyes

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