Commentary: Increased Beat-to-Beat Variability of T-Wave Heterogeneity Measured From Standard 12-Lead Electrocardiogram Is Associated With Sudden Cardiac Death: A Case-Control Study
Bibliographic record
Abstract
The electrocardiogram (ECG) reflects the electrical activity within the heart. Following the discovery of the small electrical signals in the human heart, the Dutch scientist Willem Einthoven developed sensitive methods for detecting them and recognized their clinical implications (Kligfield, 2002). For his contributions Willem Einthoven, whose scientific roots originated from the Utrecht physiology department (Einthoven, 1885), was awarded the Nobel Prize in 1924. Since then, many enigmas of the ECG have been solved and its intricate information carries valuable clues for clinical decision making. \nIn vivo mapping studies have established that spatial heterogeneity in repolarization is a requisite for re-entrant ventricular arrhythmias by effecting unidirectional conduction block. Important modulators of spatial heterogeneity of repolarization include autonomic tone, ischemia, heart rate and premature or ectopic beats, which can produce temporal heterogeneity in the order of seconds, minutes, or hours depending on their time-constants. In the contemporary era of dynamic surface electrocardiography, a formidable challenge is quantifying spatiotemporal repolarization heterogeneity with sufficient fidelity to image the arrhythmogenic myocardial substrate and thereby provide indices for a patient’s risk of arrhythmic death (Laguna et al., 2016). Hekkanen et al. (2020) performed a large case-control study of 200 victims...
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".