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Record W3117480089 · doi:10.1016/j.cjco.2020.12.011

Evaluating the 12-Lead Electrocardiogram for Diagnosing ARVC in Young Populations: Implications for Preparticipation Screening of Athletes

2020· article· en· W3117480089 on OpenAlexafffund
Cameron Landry, Meena Fatah, Kim A. Connelly, Paul Angaran, Robert M. Hamilton, Paul Dorian

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsMedicineAsymptomaticCardiologyInternal medicineAthletesInterquartile rangeSudden cardiac deathFamily historyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Arrhythmogenic right-ventricular cardiomyopathy (ARVC) is an identified cause of sport-related sudden cardiac arrest (SCA). Identifying athletes with ARVC and restricting them from exercise is believed to reduce the risk of SCA. The electrocardiogram (ECG) is considered to be an important component of screening for ARVC; however, the sensitivity of the 12-lead ECG to identify ARVC in young asymptomatic persons is unknown. METHODS: In this retrospective study, we identified 70 patients (49 ARVC-positive, based on Task Force Criteria, and 21 age-matched ARVC-negative persons from a paediatric arrhythmia database (<18 years of age); ECGs were analyzed for abnormalities, based on International Criteria for Interpretation of ECGs in Athletes, and ECG findings were adjudicated by group consensus. RESULTS: = 0.03). CONCLUSIONS: The ECG was insensitive for detecting ARVC in young (age <18 years), asymptomatic patients, and is unlikely to provide significant diagnostic value for identifying ARVC on routine preparticipation screening of adolescent athletes.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0000.000

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 head0.190
GPT teacher head0.455
Teacher spread0.265 · 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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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