Electrocardiograms in Healthy North American Children in the Digital Age
Bibliographic record
Abstract
Background: Interpretation of pediatric ECGs is limited by lack of accurate sex- and race-specific normal reference values obtained with modern technology for all ages. We sought to obtain contemporary digital ECG measurements in healthy children from North America, to evaluate the effects of sex and race, and to compare our results to commonly used published datasets. Methods: Digital ECGs (12-lead) were retrospectively collected for children ≤18 years old with normal echocardiograms at 19 centers in the Pediatric Heart Network. Patients were classified into 36 groups: 6 age, 2 sex, and 3 race (white, black, and other/mixed) categories. Standard intervals and amplitudes were measured; mean±SD and 2nd/98th percentiles were determined by age group, sex, and race. For each parameter, multivariable analysis, stratified by age, was conducted using sex and race as predictors. Parameters were compared with 2 large pediatric ECG data sets. Results: Among ECGs from 2400 children, significant differences were found by sex and race categories. The corrected QT interval in lead II was greater for girls compared with boys for age groups ≥3 years ( P ≤0.03) and for whites compared with blacks for age groups ≥12 years ( P <0.05). The R wave amplitude in V6 was greater for boys compared with girls for age groups ≥12 years ( P <0.001), for blacks compared with white or other race categories for age groups ≥3 years ( P ≤0.006), and greater compared with a commonly used public data set for age groups ≥12 years ( P <0.0001). Conclusions: In this large, diverse cohort of healthy children, most ECG intervals and amplitudes varied by sex and race. These differences have important implications for interpreting pediatric ECGs in the modern era when used for diagnosis or screening, including thresholds for left ventricular hypertrophy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".