European Society of Cardiology Core Curriculum for Cardio-Oncology
Bibliographic record
Abstract
Cardio-oncology is a rapidly growing field of cardiovascular (CV) medicine that has resulted from the continuously increasing clinical demand for specialized CV evaluation, prevention and management of patients suffering or surviving from malignant diseases. Dealing with CV disease in patients with cancer requires special knowledge beyond that included in the general core curriculum for cardiology. Therefore, the European Society of Cardiology (ESC) has developed a special core curriculum for cardio-oncology, a consensus document that defines the level of experience and knowledge required for cardiologists in this particular field. It is structured into 8 chapters, including (i) principles of cancer biology and therapy; (ii) forms and definitions of cancer therapy-related cardiovascular toxicity (CTR-CVT); (iii) risk stratification, prevention and monitoring protocols for CTR-CVT; (iv) diagnosis and management of CV disease in patients with cancer; (v) long-term survivorship programmes and cardio-oncology rehabilitation; (vi) multidisciplinary team management of special populations; (vii) organization of cardio-oncology services; (viii) research in cardio-oncology. The core curriculum aims at promoting standardization and harmonization of training and evaluation in cardio-oncology, while it further provides the ground for an ESC certification programme designed to recognize the competencies of certified specialists.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.028 |
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".