Diagnostic Work-Up of the Aortic Patient: An Integrated Approach toward the Best Therapeutic Option
Bibliographic record
Abstract
Aortic stenosis (AS) is the most common valvular heart disease. In the last decade, transcatheter aortic valve implantation (TAVI) has become the standard of care for symptomatic patients at high surgical risk. Recently, indications to TAVI have also been extended to the low surgical risk and intermediate surgical risk populations. Consequently, in this setting, some aspects acquire greater relevance: surgical risk evaluation, clinical assessment, multimodality imaging of the valve, and management of coronary artery disease. Moreover, future issues such as coronary artery re-access and valve-in-valve interventions should be considered in the valve selection process. This review aims to summarize the principal aspects of a multidimensional (multidisciplinary) and comprehensive preprocedural work-up. The Heart Team is at the center of the decision-making process of the management of aortic valve disease and bears responsibility for offering each patient a tailored approach based on an individual evaluation of technical aspects together with the risks and benefits of each modality. Considering the progressive expansion in TAVI indication and technological progress, the role of a work-up and multidisciplinary Heart Team will be even more relevant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".