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Record W2925925105 · doi:10.1002/9781119152637.ch20

Advances in Atrial Fibrillation Ablation

2019· other· en· W2925925105 on OpenAlexaff
Atul Verma, Mohammad Shenasa

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill UniversitySouthlake Regional Health CenterUniversity of Toronto
Fundersnot available
KeywordsAblationMedicineAtrial fibrillationCatheter ablationCardiologyInternal medicineSinus rhythmRandomized controlled trialCatheterPulmonary veinRefractory (planetary science)Surgery

Abstract

fetched live from OpenAlex

This chapter reviews the technological advances in catheter ablation of atrial fibrillation (AF). Pulmonary veins isolation (PVI) and electroanatomical mapping (EAM) remain the mainstays of AF ablation. The chapter summarizes the advantages, disadvantages, and potential complications of energy source and catheter during AF ablation. Based on randomized controlled trials, AF ablation is considered an important part of management as a class I level of evidence A in patients with paroxysmal AF, particularly in those refractory to antiarrhythmic therapy. Furthermore, randomized controlled trials have shown that catheter ablation is superior to medical rate control. New mapping and imaging systems and catheters have improved the success rate of AF ablation procedures. The goal of the strategies for catheter ablation of atrial fibrillation is to maintain sinus rhythm and prevent AF recurrence without significant damage to collateral tissue.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.012

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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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 designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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