Discordant echocardiographic grading in low gradient aortic stenosis (DEGAS study) from the Italian society of echocardiography and cardiovascular imaging research network: Rationale and study design
Bibliographic record
Abstract
BACKGROUND: ) in the presence of reduced (classical low-flow AS) or preserved (paradoxical low-flow AS) ejection fraction. Furthermore, the occurrence of a normal-flow LG-AS is still advocated by many authors. Within this diagnostic complexity, the diagnosis of severe AS remains challenging. OBJECTIVE: The general objective of the Discordant Echocardiographic Grading in Low-gradient AS (DEGAS Study) study will be to assess the prevalence of true severe AS in this population and validate new parameters to improve the assessment and the clinical decision-making in patients with LG-AS. METHODS AND ANALYSES: The DEGAS Study of the Italian Society of Echocardiography and Cardiovascular Imaging is a prospective, multicenter, observational diagnostic study that will enroll consecutively adult patients with LG-AS over 2 years. AS severity will be ideally confirmed by a multimodality approach, but only the quantification of calcium score by multidetector computed tomography will be mandatory. The primary clinical outcome variable will be 12-month all-cause mortality. The secondary outcome variables will be (i) 30-day mortality (for patients treated by Surgical aortic valve replacement or TAVR); (ii) 12-month cardiovascular mortality; (iii) 12-month new major cardiovascular events such as myocardial infarction, stroke, vascular complications, and rehospitalization for heart failure; and (iv) composite endpoint of cardiovascular mortality and hospitalization for heart failure. Data collection will take place through a web platform (REDCap), absolutely secure based on current standards concerning the ethical requirements and data integrity.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".