The ascending aortic aneurysm: When to intervene?
Bibliographic record
Abstract
Background: Thoracic ascending aorta aneurysms (TAA) are an important cause of mortality in adults but are a relatively less studied subject compared to abdominal aortic aneurysms (AAA). The purpose of this review is to explain the main aspects (etiology, pathophysiology, diagnosis) of this disease and to summarize the most recent developments in its management. Methodology: Literature was obtained through online health related search engines (PubMed, MEDLINE) by including the following keywords: ascending aorta aneurysm, thoracic aneurysms, Marfan syndrome, bicuspid aortic valve, familial thoracic syndrome, aortic dissection, aorta imaging and aortic aneurysm guidelines. We included articles dating from 1980 to 2014. Findings: Literature revealed how lethal this disease can be and how simple steps such as follow-up and prophylactic surgery can significantly reduce morbidity and mortality. This review also allowed us to realize the many developments that have been made in recent years in the understanding of pathologic mechanisms of this disease. Conclusion: TAA is a silent disease that needs to be recognized early in its course and followed closely in order to recommend appropriate preventive and prophylactic therapy in a timely manner.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".