The European Association for Cardio-Thoracic Surgery—Core Curriculum for the Cardiac/Cardiovascular Surgeon
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery remains a high-stakes, technically complex specialty requiring rigorous, structured training. Across Europe, significant variation exists in training pathways, assessment standards, and certification, creating disparities in preparedness among newly qualified cardiac surgeons. To address this, the European Association for Cardio-Thoracic Surgery (EACTS) developed a unified Core Curriculum for the Cardiac/Cardiovascular Surgeon. METHODS: This curriculum was developed over 12 months by a taskforce comprising experienced surgeons/trainers, educational experts, and trainee surgeons. Data were gathered through 2 National Trainee Summits (2023 and 2024), with representation from 26 national programmes. Structured feedback from these events and additional stakeholder consultations shaped the framework, which incorporates entrustable professional activities (EPAs), Canadian Medical Education Directives for Specialists (CanMEDS) competencies, and European Board of Cardiothoracic Surgery (EBCTS) syllabus. RESULTS: The curriculum defines clinical and non-clinical competencies across 3 training stages-introductory, intermediate, and advanced-aligned with EPA benchmarks. It outlines requirements for trainees, trainers, training institutions including operative volume, educational content, and assessment methodology. Institutions are encouraged to adopt competency-based progression, minimum case numbers, regular multi-source feedback, and final certification via standardized examinations such as the EBCTS. Emphasis is also placed on supporting struggling trainees, quality assurance mechanisms, and future workforce planning. CONCLUSION: The EACTS Core Curriculum offers a harmonized, flexible, and competency-based framework for cardiac surgical training. Voluntary adoption of this curriculum across Europe is expected to enhance training quality, ensure patient safety, and facilitate surgeon mobility across borders. It aims to cultivate independent, reflective, and proficient cardiac surgeons equipped for evolving clinical demands.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".