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Record W2811365615 · doi:10.1093/europace/euy067

Sex differences in cardiac arrhythmia: a consensus document of the European Heart Rhythm Association, endorsed by the Heart Rhythm Society and Asia Pacific Heart Rhythm Society

2018· review· en· W2811365615 on OpenAlexaff
Cecilia Linde, Maria Grazia Bongiorni, Ulrika Birgersdotter‐Green, Anne B. Curtis, Isabel Deisenhofer, Tetsushi Furokawa, Anne M. Gillis, Kristina H. Haugaa, Gregory Y.H. Lip, Isabelle C. Van Gelder, Marek Malík, Jeannie Poole, Tatjana Potpara, Irina Savelieva, Andrea Sarkozy, Laurent Fauchier, Valentina Kutyifa, Sabine Ernst, Estelle Gandjbakhch, Éloi Marijon, Barbara Casadei, Yi‐Jen Chen, J. Swampillai, Jodie L. Hurwitz, Niraj Varma

Bibliographic record

VenueEP Europace · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersBritish Heart Foundation
KeywordsRhythmHeart RhythmInternal medicineCardiologyMedicineHeart failure

Abstract

fetched live from OpenAlex

Cardiac arrhythmias comprise a heterogenous group of disorders ranging from benign premature heart beats to malignant sustained ventricular tachyarrhythmias. Catheter and device-based therapies coupled with landmark clinical trials have enhanced our understanding and treatment of these disorders. Studies evaluating pathophysiology, disease course, and therapeutic options for cardiac arrhythmias have been performed in predominantly male patients. Sex differences have the potential to impact diagnostic and therapeutic interventions in a wide variety of medical conditions and cardiac arrhythmias are no exception. This chapter will review sex differences in different cardiac arrhythmias with an emphasis on clinical evaluation, treatment, and outcomes.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.271
Teacher spread0.254 · 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 designNot applicable
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

Citations285
Published2018
Admission routes1
Has abstractyes

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