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Record W2313577830 · doi:10.1097/hco.0000000000000144

Atrial fibrillation and heart failure

2015· article· en· W2313577830 on OpenAlexaff
Peter Leong‐Sit, Anthony Tang

Bibliographic record

VenueCurrent Opinion in Cardiology · 2015
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsAtrial fibrillationMedicineHeart failureCardiologyInternal medicineDigoxinManagement of atrial fibrillationCardiac resynchronization therapyPopulationCatheter ablationEjection fraction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Heart failure and atrial fibrillation are both common cardiac conditions that share multiple risk factors. Heart failure is a risk for atrial fibrillation and atrial fibrillation is a risk for heart failure. The need to understand the interplay between these two cardiac conditions and the effectiveness of management options becomes increasingly relevant. RECENT FINDINGS: Recent studies have focused on the prognostic nature of atrial fibrillation and heart failure, the questionable utility of digoxin and beta-blocker therapy when heart failure and atrial fibrillation coexist, and the efficacy of cardiac ablation and resynchronization therapy with concomitant heart failure and atrial fibrillation. SUMMARY: The predominant questions that require further attention with respect to atrial fibrillation and heart failure are whether catheter ablation and rhythm control offers benefit in a high-risk heart failure population with respect to mortality or heart failure reduction, and whether cardiac resynchronization therapy implantation truly benefits the subgroup of candidate patients with permanent atrial fibrillation. Large randomized multicentre studies are currently ongoing to address these important questions.

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 score0.662
Threshold uncertainty score0.305

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 head0.156
GPT teacher head0.404
Teacher spread0.247 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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