Evaluating the 12-Lead Electrocardiogram for Diagnosing ARVC in Young Populations: Implications for Preparticipation Screening of Athletes
Bibliographic record
Abstract
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 Of the 49 ARVC-positive patients (median age: 17 [interquartile range: 16-18], 65% male), 22% were found to have abnormal ECGs; the most common ECG findings were T-wave inversions. Patients with symptoms were more likely to have abnormal ECGs than asymptomatic patients (28% compared with 17%, respectively; P = 0.002). Of 16 gene-positive patients, 31% had abnormal ECGs. Patients with abnormal ECGs had larger right-ventricular end-diastolic volume indexes on magnetic resonance imaging than those with normal ECGs ( P = 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".