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P3326Diagnostic yield of cardiovascular magnetic resonance in the screening of relatives to patients with arrhythmogenic right ventricular cardiomyopathy

2017· article· en· W2762395233 on OpenAlexaboutno aff
Rebecca Jurlander, Helen Mills, Kurt Espersen, Anna Axelsson Raja, Niels Vejlstrup, Henning Bundgaard, Alex Hørby Christensen

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyInternal medicineCardiomyopathyMagnetic resonance imagingCardiac magnetic resonanceArrhythmogenic right ventricular dysplasiaHeart failureRadiology

Abstract

fetched live from OpenAlex

Background: As a part of the revision of the Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) Task Force Criteria in 2010 (2010 TFC), more specific cardiovascular magnetic resonance (CMR) parameters were included due to the ability to provide accurate information about anatomy and functionality of the right ventricle. All ARVC probands identified at our institution are offered family screening including CMR as a part of clinical screening for ARVC. The aim of this study was to evaluate the clinical value of CMR as a part of the screening program in relatives to patients with AVRC. Methods: The retrospective cohort study registered data from relatives to ARVC probands. All included subjects have had a CMR as a part of ARVC screening in the period from January 1st 2010 to September 1st 2016. Patient data were registered using the 2010 TFC and included results from non-invasive examinations (ECG, signal-averaged ECG, Holter, echocardiogram, CMR and genetics) and baseline information such as gender, age, and occurrence of cardiovascular symptoms. CMR scans were performed on a 1.5-Tesla scanner (Magnetom Avanto, Siemens, Germany) and all Images were analyzed independently using CVI42 (Circle Cardiovascular Imaging, Canada).

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.017
GPT teacher head0.244
Teacher spread0.226 · 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.

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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