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Record W2809201382 · doi:10.1080/14779072.2018.1490644

Atrial fibrillation in young patients

2018· review· en· W2809201382 on OpenAlexaff
Jean‐Baptiste Gourraud, Paul Khairy, Sylvia Abadir, Rafik Tadros, Julia Cadrin‐Tourigny, Laurent Macle, Katia Dyrda, Blandine Mondésert, Marc Dubuc, Peter G. Guerra, Bernard Thibault, Denis Roy, Mario Talajic, Léna Rivard

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineAtrial fibrillationBrugada syndromeCardiomyopathyYoung adultPopulationIntensive care medicineManagement of atrial fibrillationPresentation (obstetrics)CardiologyPediatricsInternal medicineSurgeryHeart failure

Abstract

fetched live from OpenAlex

INTRODUCTION: Atrial fibrillation (AF) is the most frequent arrhythmia worldwide. While mostly seen in elderly, it can also affect young adults (≤ 45 years of age), older adolescent, and children. Areas covered: The aim of this review is to provide an overview of the current management of AF in young patients. Specific issues arise over diagnostic workup as well as antiarrhythmic and anticoagulation therapies. The future management and diagnostic strategies are also discussed. Expert commentary: Management of AF in the young adult is largely extrapolated from adult studies and guidelines. In this population, AF could reveal a genetic pathology (e.g. Brugada, Long QT or Short QT syndromes) or be the initial presentation of a cardiomyopathy. Therefore, thorough workup in the young population to eliminate potential malignant pathology.

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)0.002
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.023
GPT teacher head0.334
Teacher spread0.311 · 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

Citations46
Published2018
Admission routes1
Has abstractyes

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