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Record W2889342278 · doi:10.1093/eurheartj/ehy566.5048

5048Electrocardiographic and pathologic changes in young sudden death victims affected with arrhythmogenic cardiomyopathy: a clinic-pathology study

2018· article· en· W2889342278 on OpenAlexfundno aff
Monica De Gaspari, Stefania Rizzo, Gaetano Thiene, Cristina Basso

Bibliographic record

VenueEuropean Heart Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
FundersUniversità degli Studi di PadovaUniversity of CalgaryAlberta Health ServicesRegione del VenetoMedtronic
KeywordsMedicineCardiomyopathySudden deathPathologyCardiologySudden cardiac deathInternal medicineHeart failure

Abstract

fetched live from OpenAlex

Background: Arrhythmogenic cardiomyopathy (AC) is an inherited heart muscle disease at risk of ventricular arrhythmias and sudden death (SD) particularly in the young and athletes. AC is characterized by distinct morphologic, imaging and electrocardiographic (ECG) features, Purpose: To correlate the ECG changes with the morphologic findings as assessed by pathology study of the hearts of young SD victims affected by AC. Methods: Our series of young SD AC cases was searched for available ECG tracing. The presence and the site of negative T waves (precordial leads or infero-lateral), low QRS voltage or normal ECG were evaluated. The SD victims who performed competitive sports were further selected and analyzed according to pre- or post- updated AC diagnostic criteria (2000). Disease distribution in terms of right ventricular (RV), left dominant AC (LDAC) or bi-ventricular (BiV) involvement was assessed and correlated with ECG. Results: The ECG tracing was available in 49 SD victims (46 M, mean age 26 y). Involvement was RV in 7 (14%), LDAC in 12 (25%) and BiV in 29 (59%), while one had normal heart. ECG was negative in 20 (41%). Negative T waves were present in the precordial leads in 17 (35%) and in the infero-lateral leads in 8 (17%), and low voltages in 8 (17%).

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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 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
Published2018
Admission routes1
Has abstractyes

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