Year in Review in Cardiac Electrophysiology
Bibliographic record
Abstract
n the past year, our field has seen publication of a number of studies that have potential immediate and far-reaching impact on our practice. Whether considering novel forms of achieving cardiac resynchronization via His bundle pacing (HBP), randomized clinical trials validating the utility of wearable technologies or atrial fibrillation (AF) ablation, or emerging techniques for management of ventricular fibrillation, publications from several investigators, teams, and multicenter collaborations have served to further our understanding of existing disease and consider new opportunities. In putting together this list of key articles from the past year, in addition to picking the top 25 articles from Circulation: Arrhythmia and Electrophysiology, we have also focused on major studies across the published literature. Although in this review we have sought to prioritize the highest impact research, we feel it is also important to highlight several evolving areas of study that may affect the rapidity, scope, and approach to research within our field. Augmented and virtual reality integration into the electrophysiology laboratory, artificial intelligence, and rapidly miniaturizing wearable technologies have the potential to exponentially alter our field, whether in the way we practice or the way we come to new understanding of disease and its management. Given the paucity of high-impact electrophysiology-related research published in these areas to date, but the equal importance for our readership to be aware of what may be on the horizon, we have included a brief discussion of these evolving techniques and associated publications in the Data Supplement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".