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Record W4230046627 · doi:10.1016/j.hrthm.2020.05.004

European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population

2020· article· en· W4230046627 on OpenAlexaff
Jens Cosedis Nielsen, Yenn‐Jiang Lin, Márcio Jansen de Oliveira Figueiredo, Alireza Sepehri Shamloo, Alberto Alfie, Serge Bovéda, Nikolaos Dagres, Darío Di Toro, Lee L. Eckhardt, Kenneth A. Ellenbogen, Carina Hardy, Takanori Ikeda, Aparna Jaswal, Elizabeth S. Kaufman, Andrew D. Krahn, Kengo Kusano, Valentina Kutyifa, H. Lim, Gregory Y.H. Lip, Santiago Nava-Townsend, Hui‐Nam Pak, Gerardo Rodríguez-Diez, William H. Sauer, Anil Saxena, Jesper Hastrup Svendsen, Diego Vanegas, Marmar Vaseghi, Arthur A.M. Wilde, T. Jared Bunch, Alfred E. Buxton, Gonzalo Calvimontes, Tze‐Fan Chao, Lars Eckardt, Heidi Estner, Anne M. Gillis, Rodrigo Isa, Josef Kautzner, Philippe Maury, Joshua D. Moss, Gi-Byung Nam, Brian Olshansky, Luis Fernando Pava Molano, Maurício Pimentel, Mukund A. Prabhu, Wendy S. Tzou, Philipp Sommer, J. Swampillai, Alejandro Vidal, Thomas Deneke, Gerhard Hindricks, Christophe Leclercq

Bibliographic record

VenueHeart Rhythm · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsLibin Cardiovascular Institute of AlbertaMcGill University Health CentreUniversity of CalgaryUniversity of British Columbia
FundersNovo Nordisk Fonden
KeywordsMedicineHeart RhythmRhythmReferralInternal medicineCardiologyIntensive care medicineFamily medicine

Abstract

fetched live from OpenAlex

agreement not to use or recommend a treatment or procedure.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0000.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.006
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 head0.021
GPT teacher head0.294
Teacher spread0.272 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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