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P6611Pulsed field ablation - feasibility, safety and comparison to radiofrequency

2018· article· en· W2905247582 on OpenAlexaff
Atul Verma, David E. Haines, Nicole Kirchhof, Noah Barka, E Grassl, Brian Howard, Mark T. Stewart

Bibliographic record

VenueEuropean Heart Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsSouthlake Regional Health Center
Fundersnot available
KeywordsMedicineRadiofrequency ablationAblationField (mathematics)Cardiology

Abstract

fetched live from OpenAlex

Background: High voltage DC shocks produce cardiac lesions via electroporation. However, drawbacks include skeletal muscle stimulation, and heat generation. Advanced electroporation with high amplitude, pulsed electric field ablation (PFA) may provide efficacy with superior safety. Purpose: To assess safety and efficacy of pulsed field ablation (PFA) using biphasic, pulsed bipolar electric fields compared to radiofrequency ablation (RFA). Methods: Ablations were performed in 11 pigs to the superior vena cava (SVC), right atrial appendage (RAA), and right pulmonary vein (RPV) using PFA (4 biphasic trains of 120–160 pulses, 1000–1500 V) delivered from a nine-electrode circular catheter (CC). Success of electrical isolation of the SVC, RAA and RPV was tested via entrance and exit block. Phrenic nerve (PN) injury was assessed via PN pacing and monitoring of the diaphragmatic response via an accelerometer. Ablations performed in another 6 pigs were randomized to RFA (2:1 bipolar/unipolar duty cycle, 10 W/electrode max, 60°C, 60 sec) or PFA (4 biphasic trains of 60 pulses, 500V) delivered bipolar by the CC at the RPV ostium, RAA, and left atrial appendage (LAA). Local electrogram (EGM) amplitude was tested at each electrode pair before and after each ablation. Necropsies and histopathology were performed 2 weeks post ablation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.053
GPT teacher head0.356
Teacher spread0.303 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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