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Record W4313178579 · doi:10.17925/hi.2022.16.2.85

Globe Pulsed Field System for High-definition Mapping and Ablation for Atrial Fibrillation

2022· review· en· W4313178579 on OpenAlexaff
Paula Sánchez-Somonte, Atul Verma

Bibliographic record

VenueHeart International · 2022
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersBiosense WebsterFundación Alfonso Martín Escudero
KeywordsMedicineAblationPulmonary veinAtrial fibrillationCryoablationCatheter ablationRadiofrequency ablationCardiologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation ablation. Radiofrequency ablation has been the most common source of energy used to achieve PVI until now. In recent years, cryoballoon ablation has gained popularity due to its ability to perform PVI in a 'single-shot' fashion. In both cases (radiofrequency and cryoablation), the main limitation is their inability to achieve durable lesions without causing collateral damage to adjacent structures. In contrast, pulsed electric field (PEF) ablation is a non-thermal energy source that causes cell apoptosis by applying an electric current to the tissue. Lesions created by a field of energy seem to be more contiguous than traditional ablation, and the risk of damage to adjacent tissues is largely avoided due to the properties of the tissues and electrical fields. In recent years, new catheters capable of delivering PEF have been developed and are now undergoing clinical testing. In this article, we describe a complete solution for PVI: a single multielectrode catheter with 3D mapping capabilities that can deliver PEF in a single-shot PVI fashion with targeting beyond the pulmonary veins.

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)0.001
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.186
GPT teacher head0.392
Teacher spread0.205 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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