Distal anastomotic new entry tears and aortic remodeling following type A dissection repair: A systematic review
Bibliographic record
Abstract
Acute type A aortic dissection (ATAAD) is an emergent, life-threatening condition.1 Traditionally, the focus of ATAAD, both clinically and in research settings, has been on the acute and perioperative phases.The significant surgical risk, complex management, and high rates of postoperative morbidity have garnered the bulk of the attention for these patients.Our understanding of the pathophysiology and surgical management of ATAAD have evolved over the preceding decades, with a greater emphasis on longterm outcomes and aortic remodeling.[2][3][4][5][6][7] Approaches for mitigating adverse remodeling have been highlighted in the most recent Society of Thoracic Surgeons and European Association of Cardio-Thoracic Surgery aortic guidelines.8 Poor outcomes related to adverse aortic remodeling have been identified, and factors that impact adverse aortic remodeling have been uncovered.One such factor is distal anastomotic new entry tears (DANE).DANE refers to tears at the distal aortic anastomosis, thought to result from communications at the distal suture line, which create new tears allowing false lumen (FL) perfusion.DANE has been reported in up to 70% of ATAAD cases and is associated with adverse aortic remodeling and outcomes.3,[9][10][11] Commonly identified following ATAAD on appropriate postoperative imaging, numerous approaches have been undertaken in an attempt to prevent DANE and in turn facilitate improved aortic remodeling and outcomes.Here we report a systematic review of the literature focused on the development and prevention of DANE and adverse aortic remodeling following ATAAD repair. METHODS
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".