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Record W4386757859 · doi:10.1097/hco.0000000000001094

Perspectives on pulsed field ablation: how to judge endpoints

2023· review· en· W4386757859 on OpenAlexaff
Valeria Anglesio, Atul Verma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2023
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineAtrial fibrillationPulmonary vein stenosisAblationPulmonary veinPhrenic nerveCatheter ablationCardiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review highlights pulse field ablation's (PFA) significance in treating atrial fibrillation. PFA uses short-pulsed electrical fields, offering safety advantages over thermal methods. Multicenter studies' findings on PFA's safety, efficiency, and efficacy, compared with thermal techniques, are discussed. RECENT FINDINGS: The review encompasses major PFA systems utilized in multicenter studies: penta-spline, circular, and lattice catheters. These studies affirm PFA's safety, with minimal complications like esophageal injury, phrenic nerve complications, and pulmonary vein stenosis. PFA also demonstrates procedural efficiency benefits because of rapid pulse delivery. However, PFA's efficacy appears on par with thermal ablation, showing similar rates of atrial arrhythmia recurrence during follow-up periods. The studies explore diverse postablation monitoring strategies, underscoring the necessity for standardized monitoring or consistent transformation of arrhythmia data. SUMMARY: In conclusion, PFA marks a promising era for atrial fibrillation treatment with improved safety and efficiency. Efficacy is comparable to thermal methods, though technology advancements could alter this. PFA's potential as a safer and faster alternative positions it as a dominant atrial fibrillation ablation technology. Careful analysis and standardized monitoring are vital to assess PFA's potential and clinical implications.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.344
GPT teacher head0.491
Teacher spread0.146 · 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 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
Published2023
Admission routes1
Has abstractyes

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