Premature ventricular contraction-induced cardiomyopathy
Bibliographic record
Abstract
PURPOSE OF REVIEW: There has been a resurgent interest in frequent premature ventricular contractions (PVCs) led by the novel concept that they may be a potential cause of, or at least contribute to, cardiomyopathy. This review evaluates recent advances in our understanding of PVC-induced cardiomyopathy. RECENT FINDINGS: Recent studies have focused on identifying the predictors of PVC-induced cardiomyopathy, with the most consistent predictors being PVC burden and PVC QRS duration. Multiple studies have investigated the effect of catheter ablation on PVC burden and resultant left ventricular function, with the efficacy of catheter ablation and the overall PVC response rates varying between 60 and 88%. After successful ablation, the rates of improvement in left ventricular ejection fraction have varied between 47 and 100%. A recent study raises the question that perhaps even a lower PVC burden could result in PVC cardiomyopathy and adverse outcomes. SUMMARY: There is an increasing body of literature supporting a causal role of frequent PVCs in the development of left ventricular dysfunction. Effective therapy for PVCs exists; however, the optimal indications for therapy have yet to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".