Essential information on surgical heart valve characteristics for optimal valve prosthesis selection: expert consensus document from the European Association for Cardio-Thoracic Surgery (EACTS)–The Society of Thoracic Surgeons (STS)–American Association for Thoracic Surgery (AATS) Valve Labelling Task Force
Bibliographic record
Abstract
Comprehensive information on the characteristics of surgical heart valves (SHVs) is essential for optimal valve selection. Such information is also important in assessing SHV function after valve replacement. Despite the existing regulatory framework for SHV sizing and labelling, this information is challenging to obtain in a uniform manner for various SHVs. To ensure that clinicians are adequately informed, the European Association for Cardio-Thoracic Surgery (EACTS), The Society of Thoracic Surgeons (STS) and American Association for Thoracic Surgery (AATS) set up a Task Force comprised of cardiac surgeons, cardiologists, engineers, regulatory bodies, representatives of the International Organization for Standardization and major valve manufacturers. Previously, the EACTS-STS-AATS Valve Labelling Task Force identified the most important problems around SHV sizing and labelling. This Expert Consensus Document formulates recommendations for providing SHV physical dimensions, intended implant position and haemodynamic performance in a transparent, uniform manner. Furthermore, the Task Force advocates for the introduction and use of a standardized chart to assess the probability of prosthesis-patient mismatch and calls valve manufacturers to provide essential information required for SHV choice on standardized Valve Charts, uniformly for all SHV models.
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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 | 0.065 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".