The Reproducibility of Global Electrical Heterogeneity ECG Measurements
Bibliographic record
Abstract
Background: Global electrical heterogeneity (GEH) is a useful predictor of adverse clinical outcomes.However, reproducibility of GEH measurements on 10-second routine clinical ECG is unknown.Methods: Data of the prospective cohort study of incident hemodialysis patients (n=253; mean age 54.6±13.5y;56% male; 79% African American) were analysed.Two random 10-second segments of 5-minute ECG recording in sinus rhythm were compared.GEH was measured as spatial QRS-T angle, spatial ventricular gradient (SVG) magnitude and direction (azimuth and elevation), and a scalar value of SVG measured by (1) sum absolute QRST integral (SAI QRST), and (2) QT integral on vector magnitude signal (iVMQT).Bland-Altman analysis was used to calculate agreement.Results: For all studied vectorcardiographic metrics, agreement was substantial (Lin's concordance coefficient >0.98), and precision was perfect (>99.99%).95% limits of agreement were ±14º for spatial QRS-T angle, ±13º for SVG azimuth, ±4º for SVG elevation, ±14 mV*ms for SVG magnitude, and ±17 mV*ms for SAI QRST.SAI QRST and iVMQT were in substantial agreement with each other.Conclusion: Reproducibility of a 10-second automated GEH ECG measurements was substantial, and precision was perfect.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".