Specific patterns of premature beats tend to initiate ventricular tachyarrhythmias in human patients
Bibliographic record
Abstract
Introduction. Previously, we demonstrated that certain patterns of abnormal rapid beats, notably “short-long-short-short” (SLSS) patterns, tend to produce action potential block in computer models, and tend to initiate VF in in vivo canine experiments, consistent with our theory based on electrical restitution. Here we present evidence that these same patterns often precede VF in human ECG recordings. Methods. Thirty-four ECG recordings from just prior to and during tachyarrhythmic events were obtained from ICDs implanted in several human patients. The distributions of the first four abnormal RR intervals prior to arrhythmia onset were fit to single-gaussian and dual-gaussian distributions. Results. Dual-gaussian distributions were obtained for the second and third abnormal beats, while single gaussian distributions were obtained for the first and fourth. These distributions are consistent with the tendency of the SLSS pattern of premature beats, as well as SLLS and SSSS patterns, to precede the tachyarrhythmic event, as described by our computer model. Conclusions. The results provide further evidence that electrical restitution theory, the basis for both our theory and computer model, although imperfect, is sufficient to both predict and understand the manner in which premature beats initiate VF. This understanding may, in the future, lead to new methods for preventing VF, through the imposition of stimuli designed to avoid the dangerous premature beat patterns described in this study.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".