Not All SAVR Are Created Equal: All the Approaches Available for Surgical Aortic Valve Replacement
Bibliographic record
Abstract
Surgical Aortic Valve Replacement (SAVR) is still one of the pillars of cardiac surgery practice, and its role is evolving into a more complex operation. The competition with structural valve therapies and the urgent demand for less invasive solutions have unleashed surgeons' creativity in adapting to these new challenges. All the possible ways to surgically replace the aortic valve are analyzed in this review. Surgical techniques, advantages and disadvantages, and key differences are listed, helping surgeons navigate the available options. Sternotomy SAVR is the benchmark, but that is becoming obsolete and, in some cases, no longer performed for teaching purposes. Mini sternotomy is the easiest way to achieve minimal invasiveness in all anatomic situations, while right anterior thoracotomy is an elegant solution mastered by fewer surgeons. Endoscopic and robotic-assisted techniques are shaping the future of SAVR, yet they still lack wide adoption. The choice of approach is mainly dictated by the anatomic features of the patient and the surgeon's skills. A flow diagram to overcome the learning curve and advance toward more complex surgery is provided here. Mastering as many techniques as possible is paramount when offering a patient-tailored approach and performing a safe and less invasive operation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".