The Measurement of the QT and QTc on the Neonatal and Infant Electrocardiogram: A Comprehensive Reliability Assessment
Bibliographic record
Abstract
BACKGROUND: An electrocardiogram has been proposed to screen for prolonged QT interval that may predispose infants to sudden death in the first year of life. Understanding the reliability of QT interval measurement will inform the design of a screening program. METHODS: Three pediatric cardiologists measured the QT/RR intervals on 60 infant electrocardiograms (median age 46 days), from leads II, V5 and V6 on three separate occasions, 7 days apart, according to a standard protocol. The QTc was corrected by Bazett's (QTcB), Fridericia's (QT(CFrid)), and Hodges' (QTcH) formulae. Intraobserver and interobserver reliability were assessed by intraclass correlation coefficients (ICC), limits of agreement and repeatability coefficients for single, average of two and average of three measures. Agreement for QTc prolongation (> 440 msec) was assessed by kappa coefficients. RESULTS: QT interval intraobserver ICC was 0.86 and repeatability coefficient was 25.9 msec; interobserver ICC increased from 0.88 for single observations to 0.94 for the average of 3 measurements and repeatability coefficients decreased from 22.5 to 16.7 msec. For QTcB, intraobserver ICC was 0.67, and repeatability was 39.6 msec. Best interobserver reliability for QTcB was for the average of three measurements (ICC 0.83, reproducibility coefficient 25.8 msec), with further improvement for QTcH (ICC 0.92, reproducibility coefficient 16.69 msec). Maximum interobserver kappa for prolonged QTc was 0.77. Misclassification around specific cut points occurs because of the repeatability coefficients. CONCLUSIONS: Uncorrected QT measures are more reliable than QTcB and QT(CFrid). An average of three independent measures provides the most reliable QT and QTc measurements, with QTcH better than QTcB.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".