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Record W4206107483 · doi:10.1016/j.cjco.2022.01.001

Arrhythmia Monitoring for Risk Stratification in Hypertrophic Cardiomyopathy

2022· review· en· W4206107483 on OpenAlexafffund
Darson Du, Christopher O.Y. Li, Kevin Ong, Ashkan Parsa, Adaya Weissler‐Snir, Jeffrey B. Geske, Zachary Laksman

Bibliographic record

VenueCJC Open · 2022
Typereview
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BC
KeywordsHypertrophic cardiomyopathyRisk stratificationMedicineCardiologyInternal medicineAmbulatoryCardiomyopathyAmbulatory ECGCardiac arrhythmiaIntensive care medicineHeart failureAtrial fibrillation

Abstract

fetched live from OpenAlex

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy, presenting significant clinical heterogeneity. Arrhythmia risk stratification and detection are critical components in the evaluation and management of all cases of HCM. The 2020 American Heart Association/American College of Cardiology HCM guidelines provide new recommendations for periodic 24-48-hour ambulatory electrocardiogram monitoring to screen for atrial and ventricular arrhythmias. A strategy of more frequent or prolonged monitoring would lead to earlier arrhythmia recognition and the potential for RSUM La cardiomyopathie hypertrophique (CMH) qui est la cardiomyopathie h er editaire la plus fr equente pr esente une h et erog en eit e clinique importante. La stratification du risque d'arythmies et leur d etection sont des composantes essentielles de l' evaluation et de la prise en charge de tous les cas de CMH. Les lignes directrices 2020 de l'American Heart Association et de l'American College of Cardiology en matire de CMH fournissent les nouvelles recommandations sur la surveillance p eriodique de l' electrocardiogramme ambulatoire de 24-48 heures pour d epister les arythmies auriculaires et ventriculaires.

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)0.001
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 score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.112
GPT teacher head0.387
Teacher spread0.274 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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