The Management of the Aortic Arch in Type A Aortic Dissection: Replace, Repair with the AMDS, or Leave for Another Day?
Bibliographic record
Abstract
OBJECTIVES: Acute type A aortic dissection (ATAAD) is a life-threatening condition that requires emergent surgical intervention. Numerous surgical approaches exist for ATAAD, and controversy remains regarding the optimal arch interventions for ATAAD patients. Aortic Arch Interventions: Approaches to ATAAD repair include hemiarch repair or extended arch repairs, including the hemiarch with a hybrid stent implantation, such as the AMDS hybrid Prosthesis, total arch replacement (TAR), and the use of an elephant trunk and frozen elephant trunk. While indications for each procedure exist, such as entry tears in the arch, arch aneurysms, and head vessel communications for TAR and malperfusion and a reduced risk of distal anastomotic new entry tears in Debakey I aortic dissection for the AMDS and frozen elephant trunks, the optimal intervention depends on numerous factors. Surgeon and center experience, resource availability, patient risk, and anatomy all contribute to the decision-making process. TAR has improved in safety over the years and has been demonstrated to be comparable to the hemiarch repair in terms of safety in many settings. TAR may also prevent adverse remodeling and can effectively treat more distal diseases, the presence of arch tears, arch aneurysms, and branch vessel involvement or malperfusion. CONCLUSIONS: Numerous surgical approaches exist to manage ATAAD, allowing for the surgeon to tailor the repair to the individual patient and pathology. TAR allows for single or staged repair of extensive pathologies and can address distal entry tears, the aneurysmal arch, and head vessel pathologies. In cases with malperfusion, an AMDS can be used in many cases. The management strategy for ATAAD should always involve performing the best surgery for the patient, although in cases where a total arch is indicated but cannot be performed safely by a non-aortic surgeon, the safest approach may be to perform a hemiarch initially and to plan for an elective arch reoperation in the case it is required following close surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".