Bibliographic record
Abstract
PURPOSE OF REVIEW: The medical and surgical management of aortic disease is continually changing in search for improved outcomes. Our objective is to highlight recent advances in a few select areas pertaining to aortic disease and aortic surgery: the genetics of aortopathy, medical therapy of aortic aneurysms, advances in cardiac imaging, and operative strategies for the aortic arch. RECENT FINDINGS: As our understanding of the genetic basis for aortopathy continues to improve, routine genetic testing may be of value in assessing patients with genetically triggered forms of aortic disease. With regard to medical advances, treating patients with Marfan syndrome with either losartan or atenolol at an earlier stage in their disease course improves outcomes. In addition, novel imaging indices such as wall shear stress and aortic stiffness assessed by MRI may become useful markers of aortopathy and warrant further study. With regard to the optimal technique for cerebral perfusion in aortic arch surgery, high-quality data are still lacking. Finally, in patients with complex, multilevel aortic disease, the frozen elephant trunk is a viable single-stage option compared with the conventional elephant trunk, although with an increased risk for spinal cord injury. SUMMARY: Based on recent advances, continued studies in genetics, cardiac imaging, and surgical trials will further elucidate the etiology of aortopathy and ultimately guide management, both medically and surgically.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".