European survey on valvular heart disease clinical experience from the European Society of Cardiology council on valvular heart disease
Bibliographic record
Abstract
Aims: The aim of this survey is to analyze how current recommendations on valvular heart disease (VHD) management have been adopted. Identifying potential discrepancies between recommendations and everyday clinical practice would enable us to better understand and address the remaining challenges in this controversial and complex field. Methods and results: A total of 33 questions, distributed via email to all European Society of Cardiology (ESC) affiliated countries through the newsletter of the ESC council on VHD, were answered by 689 respondents, mainly from tertiary care settings. The results of this survey showed that VHD patients are mostly managed by tertiary care centres, where multi-disciplinary heart teams are frequently a reality. Cardiac computed tomography (CT) is often used in the preprocedural planning of transcatheter interventions, particularly for sizing and deliverability assessment. Echocardiography represents the most widely used imaging modality in the diagnostic, intra-operative and follow-up phase of VHD patients. Cardiac magnetic resonance (CMR) is still largely underused, also for conditions such as mitral annular disjunction, or for the assessment of left ventricle volumes where it is considered as the gold standard, despite 3D volumes by echocardiography having proved good comparability with CMR. As for endocarditis, despite still underused, transesophageal echocardiography (TEE) represents the approach of choice for the diagnosis of native and prosthesis valve endocarditis (up to 46% of the respondents use it). In this context, positron emission tomography-CT is largely underused. Conclusion: There is widespread adoption of current recommendation on the evaluation of VHD and these are frequently used to guide patient management. Nonetheless, there are still many discrepancies across centres and countries which need to be addressed with the aim of improving patients' management and outcomes and ultimately positively impacting on healthcare resources.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".