The Aortic Team Model for the Management of the Distal Arch, Descending Thoracic and Thoracoabdominal Aorta: Appraisal at 3 Years
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess feasibility, logistical challenges, and clinical outcomes associated with the implementation of an Aortic Team model for the management of distal arch, descending thoracic and thoracoabdominal aortic disease. METHODS: An Aortic Team care pathway was implemented in November 2019. Working as a unit, two cardiac surgeons, two vascular surgeons, an interventional radiologist, a cardiologist, and an anesthesiologist collectively determined care decisions via multispecialty presence at an Aortic Clinic. Cardiac and vascular surgeons operated in tandem for open procedures. Interventional radiology participated alongside cardiac and vascular for endovascular procedures. Cardiology aided in medical therapies for heritable and degenerative disease, and had a lead role for genetics and high-risk pregnancy referrals. The model spanned three hospitals. Clinical outcomes at 3 years were assessed. RESULTS: There were 35 descending thoracic and thoracoabdominal surgeries and 77 thoracic endovascular aortic repairs. Endoarch devices were used in 7 cases (Gore Thoracic Branch Endoprosthesis, 4, Terumo RelayBranch, 3) and an endothoracoabdominal device in 4 cases (Cook Zenith t-branch). The Aortic Clinic acquired 456 patients, with yearly increases (54 patients [year 1], 181 patients [year 2], 221 patients [year 3]). For surgery, mortality was 8.6% (3/35), permanent paralysis 5.7% (2/35), stroke 8.6% (3/35), permanent dialysis 0%, and reinterventions 8.6% (3/35). For endovascular cases, mortality was 3.9% (3/77), permanent paralysis 3.9% (3/77), stroke 5.2% (4/77), permanent dialysis 1.3% (1/77), and reinterventions 16.9% (13/77). CONCLUSION: An Aortic Team model is feasible and ensures all treatment options are considered. Conventional open thoracoabdominal procedures showed acceptable outcomes. Endoarch technology shows early promise.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".