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Record W2799918332 · doi:10.1093/europace/euw348

OUP accepted manuscript

2016· article· en· W2799918332 on OpenAlexaff
Mirabeau Saha, Giulio Conte, Maria Luce Caputo, François Regoli, Rolf Krause, Angelo Auricchio, Vincent Jacquemet

Bibliographic record

VenueEP Europace · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsAblationMedicinePulmonary veinSinus rhythmCardiologyTorsoInternal medicineCatheter ablationAtrial fibrillationAnatomy

Abstract

fetched live from OpenAlex

AIMS: Apparently conflicting clinical measurements of P-wave duration (PWD) pre- vs. post-ablation have been reported. To assist the interpretation of these clinical data, we used a computer model of the atria and torso to simulate P waves before and after pulmonary vein (PV) isolation. METHODS AND RESULTS: Twenty ablation patterns were designed (segmental or ipsilateral ablation; five distances to PV sleeves; addition of a roof line or not). Possible PV reconnections were introduced as gaps in the ablation lines. PWD and area were measured during sinus rhythm in vectorcardiogram (VCG) magnitude signals and on the 16-lead ECG before and after ablation, and after PV reconnection. After PV isolation, biatrial activation time was prolonged by 0-5 ms without and by 48±5 ms with roof line. Yet PWD was shortened in lead V3 and V4 by up to 15 ms. The effect of ablation on P-wave morphology was stronger when larger PV areas were isolated. Segmental and ipsilateral PV isolation led to concordant results. P-wave area increased in V1 and decreased in V6. Changes in PWD and area on the VCG were sensitive to the threshold used for detecting the end of the P wave. The occurrence of PV reconnection was best identified on leads V3, V4, and V9. CONCLUSION: PV isolation and reconnection induced measurable changes on the 16-lead ECG that might be used to improve patient follow-up after 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.002

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations11
Published2016
Admission routes1
Has abstractyes

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