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Record W3093943051 · doi:10.1093/europace/euaa217

Atrial fibrillation ablation in heart failure: What do we know? What can we do?

2020· review· en· W3093943051 on OpenAlexaff
Andrea Chiocchini, María Terricabras, Atul Verma

Bibliographic record

VenueEP Europace · 2020
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsSouthlake Regional Health Center
Fundersnot available
KeywordsMedicineAtrial fibrillationEjection fractionCardiologySinus rhythmInternal medicineCatheter ablationHeart failureAblationStroke (engine)Randomized controlled trial

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF) are two conditions that frequently impact reciprocally on each other. Patients with HFrEF have an increased risk of stroke, hospitalization and mortality after they develop AF and vice versa, AF causing deterioration of the ejection fraction is also associated to increased mortality. Catheter ablation has emerged as an effective alternative to antiarrhythmic drug treatment to maintain sinus rhythm and some randomized trials have shown a potential benefit in terms of mortality and hospitalization. This review discusses the available evidence regarding catheter ablation treatment in this specific patient group.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.065
GPT teacher head0.351
Teacher spread0.286 · 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 designOther design
Domainnot available
GenreReview

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

Citations9
Published2020
Admission routes1
Has abstractyes

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