An assessment of the current medical management of thoracic aortic disease: A patient-centered scoping literature review
Bibliographic record
Abstract
Thoracic aortic aneurysm and dissection are complex diagnoses that require management by multidisciplinary providers using a variety of medical therapies, surgical interventions, and lifestyle modifications. Pharmacological agents, such as β-blockers (atenolol) and angiotensin II type 1 receptor blockers (losartan), have been mainstay treatments for several years, and research from the past decade has continued to evaluate these and other medication classes to further improve patient morbidity and mortality. Combination β- and renin-aldosterone-angiotensin blockade, statins, metformin, antioxidants, and vitamins have been evaluated as therapeutics in both thoracic and abdominal aortic aneurysms, as well as the effects of various antibiotics (ie, fluoroquinolones and tetracyclines) and benefits of lifestyle modifications (eg, diet and exercise) and enhanced patient-centered care and treatment adherence. In addition, as our understanding of the genetic, biochemical, and pathophysiological mechanisms behind these diseases expands, so do potential targets for future therapeutic research (eg, interleukins, matrix metalloproteases, and mast cells). This review incorporates the major meta-analyses, systematic and generalized reviews, and clinical trials published from 2010 through 2021 that focus on these topics in thoracic aortic aneurysms (and abdominal aneurysms when thoracic literature is scarce). Several key ongoing clinical trials, case studies, and in vivo/in vitro studies are also mentioned. Furthermore, we discuss current gaps in the literature and the abundance of clinical evidence for some interventions in abdominal aneurysms with few thoracic correlates, thus indicating a need for investigation of these subjects in the latter.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".