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Record W4389788020 · doi:10.1161/circep.123.012072

Clinical Management of Brugada Syndrome: Commentary From the Experts

2023· review· en· W4389788020 on OpenAlexaff
Michael J. Cutler, Lee L. Eckhardt, Elizabeth S. Kaufman, Elena Arbelo, Elijah R. Behr, Pedro Brugada, Marina Cerrone, Lia Crotti, Carlo de Asmundis, Michael H. Gollob, Minoru Horie, David T. Huang, Andrew D. Krahn, Barry London, Steven A. Lubitz, Judith A. Mackall, Koonlawee Nademanee, Marco Pérez, Vincent Probst, Dan M. Roden, Frédéric Sacher, Georgia Sarquella‐Brugada, Melvin M. Scheinman, Wataru Shimizu, Benjamin Shoemaker, Raymond W. Sy, Atsuyuki Watanabe, Arthur A.M. Wilde

Bibliographic record

VenueCirculation Arrhythmia and Electrophysiology · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of British ColumbiaUniversity Health Network
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthPfizerBristol-Myers SquibbInvitaeAmerican Heart Association
KeywordsBrugada syndromeGuidelineAsymptomaticMedicineIntensive care medicineRisk managementSudden cardiac deathPediatricsCardiologyInternal medicinePathologyManagementEconomics

Abstract

fetched live from OpenAlex

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.361
Teacher spread0.310 · 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 designOther design
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

Citations13
Published2023
Admission routes1
Has abstractyes

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