Defining cardiovascular toxicities of cancer therapies: an International Cardio-Oncology Society (IC-OS) consensus statement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it