A Primer on the Evolving Subspecialty of Onco-Electrophysiology
Bibliographic record
Abstract
Cardio-oncology has become a well-established subspecialty because of the growing burden of cardiovascular diseases in oncology patients, resulting from the cardiac toxicities of cancer therapies and the coexistence of both conditions in the same population. As with other cardiovascular conditions, cardiac arrhythmias have emerged as an important concern in patients with cancer. However, the management of arrhythmias is more complicated in these patients because of complex interactions between oncotherapeutics and arrhythmia-treatment strategies. Similarly, patients with cardiac implantable electronic devices (CIEDs) require cancer treatment strategies that involve radiation therapy require specific management strategies. Thus, there is a need for a specific mechanistic understanding of electrophysiological abnormalities, arrhythmia, and device management in oncology patients, especially given the expanding range of oncologic therapies and radiation strategies. This increasingly prevalent clinical challenge requires new expertise that expands on a yearly basis. This narrative review deals with this recent expansion and addresses key areas of onco-electrophysiology, including the mechanistic basis of common electrocardiographic changes, diagnosis, and management of arrhythmias attributable to oncotherapeutics and the care of patients with arrhythmias who require oncologic therapies, especially patients with devices and drug interactions leading to arrhythmias as seen by cardiac physicians dealing with oncology patients. In addition, it reviews evolving management strategies and protocols for patients with implantable devices, especially if urgent radiation is needed. This review aims to bridge the recent knowledge growth in arrhythmia care for patients with cancer and highlight the evolution of onco-electrophysiology as a subspeciality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".