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Record W1515174586

Abstract 16452: Determinants of Vt Occurrence in Patients With Nonischemic and Dilated Cardiomyopathy: Insights From the Study of Explanted Human Hearts

2014· article· en· W1515174586 on OpenAlexaff
Lucas Valtuille, Nirmal Parajuli, Konrad S. Famulski, Philip F. Halloran, Sergi Consolato, Gavin Y. Oudit

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCardiologyInternal medicineVentricular tachycardiaDilated cardiomyopathyImplantable cardioverter-defibrillatorVentricular fibrillationEjection fractionVentricular remodelingHeart failureCardiomyopathyFibrillationAtrial fibrillation
DOInot available

Abstract

fetched live from OpenAlex

Introduction: The molecular and cellular determinants of ventricular tachycardia (VT) in patients with non-ischemic dilated cardiomyopathy (NIDCM) remain poorly defined. Hypothesis: Patients with idiopathic NIDCM and significant VT display different tissue and electrical remodeling when compared to other NIDCM patients with end stage heart failure but no history of ventricular arrhythmias. Methods: History of clinically-significant VT was defined as symptomatic VT, cardiac arrest with documented VT/ventricular fibrillation or appropriate defibrillator shocks. A total of 21 explanted hearts were analyzed (VT=10, no VT=11) in a double-blind, case control study design. The molecular, cellular and histological features of adverse myocardial remodeling were assessed. Results: The two groups were closely matched in sex (male= 80% vs 72%), LVEF (20.8% vs 20.6%, p=0.6), LVAD use (4/10 vs 6/11, p=0.66), and optimal medical therapy. Explanted hearts from patients with VT showed greater hypertrophic changes based on...

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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
Published2014
Admission routes1
Has abstractyes

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