Current Status of CT Imaging Before Common Transcatheter Interventions for Structural Heart Disease
Bibliographic record
Abstract
Background: Over the past decade, several trials and observational studies have validated the use of minimally invasive cardiac interventions as viable treatment options for various cardiac diseases. Transcatheter techniques for severe aortic valve stenosis have rapidly emerged as alternatives to surgical aortic valve replacement in certain patient populations. Additionally, non-surgical treatment options have expanded for conditions affecting other cardiac valves, such as the mitral valve. These emerging minimally invasive interventions complement already well-established endovascular techniques for, among others, atrial septal defect closure, left atrial appendage occlusion and pulmonary vein isolation in patients with atrial fibrillation. Given their non-surgical nature and lack of direct visualisation of the targeted anatomy, these procedures heavily rely on precise pre-procedural radiological imaging for optimal patient selection and procedural success. Method: This paper is based on the expert opinion of the authors and an exhaustive literature research. Results: This manuscript reviews the most commonly employed minimally invasive cardiac interventions, highlighting the essential pre-procedural imaging information and key aspects that must be included in radiological reports to mitigate potential complications. Conclusion: Accurate pre-procedural imaging is crucial for ensuring safe and effective minimally invasive cardiac interventions, underscoring the importance of the radiologist in the pre-procedural work-up of these patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".