The Call for the “Interventional/Hybrid” Aortic Surgeon: Open, Endovascular, and Hybrid Therapies of the Aortic Arch
Bibliographic record
Abstract
Aortic arch pathology is relatively rare but potentially highly fatal and associated with considerable comorbidity. Operative mortality and complication rates have improved over time but remain high. In response, aortic arch surgery is one of the most rapidly evolving areas of cardiac surgery in terms of surgical volume and improved outcomes. Moreover, there has been a surge in novel devices and techniques, many of which have been developed by or codeveloped with vascular surgeons and interventional radiologists. Nevertheless, the extent of arch surgery, the choice of nadir temperature, cannulation, and perfusion strategies, and the use of open, endovascular, or hybrid options vary according to country, centre, and surgeon. In this review article, we provide a technical overview of the surgical, total endovascular, and hybrid repair options for aortic arch pathology through historical developments and contemporary results. We highlight key information for surgeons, cardiologists, and trainees to understand the management of patients with aortic arch pathology. We conclude by discussing training paradigms, the role of aortic teams, and gaps in knowledge, arguing for the need for wire skills for the future "interventional aortic surgeon" and increased research into techniques and novel devices to continue improving outcomes for aortic arch surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".