Defining cardiovascular toxicities of cancer therapies: an International Cardio-Oncology Society (IC-OS) consensus statement
Bibliographic record
Abstract
The discipline of Cardio-Oncology has seen tremendous growth over the past decade. It is devoted to the cardiovascular (CV) care of the cancer patient, especially to the mitigation and management of CV complications or toxicities of cancer therapies, which can have profound implications on prognosis. To that effect, many studies have assessed CV toxicities in patients undergoing various types of cancer therapies; however, direct comparisons have proven difficult due to lack of uniformity in CV toxicity endpoints. Similarly, in clinical practice, there can be substantial differences in the understanding of what constitutes CV toxicity, which can lead to significant variation in patient management and outcomes. This document addresses these issues and provides consensus definitions for the most commonly reported CV toxicities, including cardiomyopathy/heart failure and myocarditis, vascular toxicity, and hypertension, as well as arrhythmias and QTc prolongation. The current document reflects a harmonizing review of the current landscape in CV toxicities and the definitions used to define these. This consensus effort aims to provide a structure for definitions of CV toxicity in the clinic and for future research. It will be important to link the definitions outlined herein to outcomes in clinical practice and CV endpoints in clinical trials. It should facilitate communication across various disciplines to improve clinical outcomes for cancer patients with CV diseases.
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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 | 0.066 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".