ESC Working Group on Valvular Heart Disease Position Paper--heart valve clinics: organization, structure, and experiences
Bibliographic record
Abstract
BACKGROUND: With an increasing prevalence of patients with valvular heart disease (VHD), a dedicated management approach is needed. The challenges encountered are manifold and include appropriate diagnosis and quantification of valve lesion, organization of adequate follow-up, and making the right management decisions, in particular with regard to the timing and choice of interventions. Data from the Euro Heart Survey have shown a substantial discrepancy between guidelines and clinical practice in the field of VHD and many patients are denied surgery despite having clear indications. The concept of heart valve clinics (HVCs) is increasingly recognized as the way to proceed. At the same time, very few centres have developed such expertise, indicating that specific recommendations for the initial development and subsequent operating requirements of an HVC are needed. AIMS: The aim of this position paper is to provide insights into the rationale, organization, structure, and expertise needed to establish and operate an HVC. Although the main goal is to improve the clinical management of patients with VHD, the impact of HVCs on education is of particular importance: larger patient volumes foster the required expertise among more senior physicians but are also fundamental for training new cardiologists, medical students, and nurses. Additional benefits arise from research opportunities resulting from such an organized structure and the delivery of standardized care protocols. CONCLUSION: The growing volume of patients with VHD, their changing characteristics, and the growing technological opportunities of refined diagnosis and treatment in addition to the potential dismal prognosis if overlooked mandate specialized evaluation and care by dedicated physicians working in a specialized environment that is called the HVC.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".