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Record W3216019709 · doi:10.1101/2021.11.22.21266568

The Substrate of Sudden Death in Long-QT Syndrome is localized in the Epicardium

2021· preprint· en· W3216019709 on OpenAlexaff
Carlo Pappone, Giuseppe Ciconte, Luigi Anastasia, Valeria Borrelli, Edward R. Grant, Gabriele Vicedomini, Vincenzo Santinelli

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of British Columbia
FundersMinistero della Salute
KeywordsMedicineLong QT syndromeCardiologySudden cardiac deathVentricleInternal medicineCatheter ablationSudden deathQT intervalAblation

Abstract

fetched live from OpenAlex

ABSTRACT Despite significant advances in the prevention of cardiovascular diseases, sudden cardiac death (SCD) persists as a major public health problem. Among young and apparently healthy individuals, Long-QT syndrome (LQTS) represents a leading progenitor of SCD owing to fatal ventricular arrhythmia. Scientific understanding of this association has grown in recent years, and the mortality rate after LQTS diagnosis has significantly decreased. However, despite medical treatment advances, life-threatening ventricular arrhythmias still occur. Until now, no research has established the degree to which this inherited condition arises from an underlying arrhythmogenic electroanatomical substrate. Here, we present direct evidence showing that LQTS patients who survive spontaneous malignant arrhythmias harbor structural electrophysiological abnormalities localized in the epicardium of the right ventricle. We further show that the elimination of these abnormalities by means of catheter ablation successfully suppresses malignant arrhythmias, offering a new approach for the effective treatment of LQTS patients.

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)0.000
metaresearch head score (Gemma)0.000
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
Metaresearch0.0000.000
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)0.0020.000

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 head0.017
GPT teacher head0.279
Teacher spread0.261 · 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
Published2021
Admission routes1
Has abstractyes

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Same venuemedRxiv→Same topicCardiac electrophysiology and arrhythmias→French-language works237,207→