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Record W7116389706 · doi:10.14740/cr2131

Exercise Oscillatory Ventilation: A Potential New Risk Factor for Sudden Cardiac Death in Hypertrophic Cardiomyopathy

2025· article· en· W7116389706 on OpenAlexvenueno aff
Stefanos Sakellaropoulos, Muhemin Mohammed, Panagiotis Sakellaropoulos, Muhammad Ali, Athanasios Papadis, Ilias Piperopoulos, Eugenia Kloufetou, Benedict Schulte Steinberg, Claire Rogers, Andreas Mitsis

Bibliographic record

VenueCardiology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic cardiomyopathySudden cardiac deathHeart failureRisk factorEjection fractionSudden deathCardiac magnetic resonance imaging

Abstract

fetched live from OpenAlex

Other than the traditional risk factors for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) - detected by means of anamnesis, Holter monitoring, exercise testing, echocardiography and cardiac magnetic resonance imaging - exercise oscillatory ventilation (EOV), detected by cardiopulmonary exercise testing (CPET), has recently been observed in patients with HCM. EOV is considered as one of the most important independent risk factors for morbidity, mortality and SCD in patients with reduced, as well as with preserved ejection fraction. Considering HCM as a prototype of heart failure with preserved ejection fraction, we would like to present a short, specific review concerning EOV as a potential new risk factor for SCD in HCM.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.048
GPT teacher head0.359
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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