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Record W4301391556 · doi:10.1161/jaha.122.027385

International Practice Patterns in the Detection and Management of Arrhythmias in Patients With Hypertrophic Cardiomyopathy

2022· letter· en· W4301391556 on OpenAlexaff
Matthew Cheung, Ali Husain, Darson Du, Christopher O. Y. Li, Jeremy Parker, Adaya Weissler‐Snir, Jeffrey B. Geske, Kevin Ong, Zachary Laksman

Bibliographic record

VenueJournal of the American Heart Association · 2022
Typeletter
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHypertrophic cardiomyopathyCardiologyCardiomyopathyInternal medicineSudden cardiac deathClinical PracticeIntensive care medicineHeart failureFamily medicine

Abstract

fetched live from OpenAlex

trial fibrillation (AF) occurs in 22% to 32% of patients with hypertrophic cardiomyopathy (HCM), 1,2 with ≈50% experiencing subclinical/ asymptomatic AF on cardiac rhythm monitoring devices, including implantable loop recorders, implantable cardioverter-defibrillators (ICDs), and permanent pacemakers.3 Nonsustained ventricular tachycardia (NSVT) is associated with a higher risk of sudden cardiac death in patients with HCM. 4 Faster, longer, and repetitive episodes of NSVT may confer greater risk.5 Current guidelines do not directly address the role of different arrhythmia screening strategies (screening frequency, role of extended monitoring, wearable devices) and optimal management of detected arrhythmias.We conducted an international multicenter survey to evaluate practice patterns and expert opinions regarding AF and NSVT screening and management at HCM comprehensive care centers. 1 Survey questions were developed by experts with established practices in cardiac electrophysiology (ZL and AWS) and HCM (AWS, KO, and JG).The current study was approved by the University of British Columbia (UBC) research ethics board.This survey was generated and distributed by the Canadian-hosted Qualtrics UBC Survey tool.This survey was anonymously and individually distributed to a predetermined list of 34 clinical experts working at HCM comprehensive care centers in North America, Australia, and Europe in May 2021.Informed consent was obtained at the beginning of the survey.Anonymized data underwent descriptive statistics and visualization using the survey tool.The data that support the findings of this study are available from the corresponding author on reasonable request.We received 23 full and 1 partial response (Table ), of 34, from experts in Canada (n=5), the United States (n=10), Germany (n=1), Denmark (n=1), Italy (n=1), Australia (n=1), Spain (n=1), Switzerland (n=1), and the United Kingdom (n=2).Most respondents (65.2%) had >10 years of practice experience.Each center evaluates a mean of 265 (SD, 165) new patients with HCM annually.Routine screening was performed by 87% of respondents.Most experts (78%) considered left atrial dilatation an important factor when considering screening.Consumer wearable devices were the third most used screening tool (56%) and 91% would

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)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations3
Published2022
Admission routes1
Has abstractno

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