Therapy for ventricular arrhythmias in structural heart disease: a multifaceted challenge
Bibliographic record
Abstract
The unpredictable nature and potentially catastrophic consequences of ventricular arrhythmias (VAs) have obligated physicians to search for therapies to prevent sudden cardiac death (SCD). At present, a low left ventricular ejection fraction (LVEF) has been used as a risk factor to predict SCD in patients with structural heart disease and has been consistently adopted as the predominant, and sometimes sole, indication for implantable cardioverter defibrillator (ICD) therapy. Although the ICD remains the mainstay life-saving therapy for SCD, it does not modify the underlying arrhythmic substrate and may be associated with adverse effects from perioperative and long-term complications. Preventative pharmacological therapy has been associated with limited benefits, but anti-arrhythmic medications have significant side effects profiles. Catheter ablation of VAs has greatly evolved over the last few decades. Substrate mapping in sinus rhythm has allowed haemodynamically unstable VAs to be successfully treated. Both LVEF as an indication for ICD therapy and electro-anatomical mapping for substrate modification identify static components of underlying myocardial arrhythmogenicity. They do not take into account dynamic factors, such as the mechanisms of arrhythmia initiation and development of new anatomical or functional lines of block, leading to the initiation and maintenance of VAs. Dynamic factors are difficult to evaluate and consequently are not routinely used in clinical practice to guide treatment. However, progress in the treatment of VAs should consider and integrate dynamic factors with static components to fully characterize the myocardial arrhythmic substrate.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".