Engineering Approach to Study the Effect of TEVAR on the Cardiovascular System: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: To study the effect of endovascular treatment of the thoracic aorta on cardiac geometry and evaluate the effects of stent placement on hemodynamics and cardiovascular biomechanics. METHODS: Articles were selected through the use of online databases such as PubMed, Scopus, and Web of Science, investigating the use of engineering methods (computational analysis and simulations using three-dimensional models of cardiovascular structures obtained from medical imaging) to study the effects of pretreatment and posttreatment Thoracic Endovascular Aortic Repair (TEVAR) in terms of left ventricular mass variation and assessment of fluid dynamics parameters such as Wall Shear Stress (WSS), flow variations, and velocity. The quality of the included studies was assessed using the Newcastle-Ottawa scale. RESULTS: A total of 11 studies were considered: 3 reported data on left ventricular mass variation, 5 reported flow and velocity variations, and 6 provided information on WSS. A high discrepancy in results and methodology for conducting the analyses was observed. Overall, an increase in left ventricular mass was observed in patients undergoing TEVAR, while an improvement in flow conditions and stress was noted following the exclusion of the pathological aortic zone. CONCLUSIONS: To summarize, TEVAR can result in changes in vascular structures. However, the current literature on this topic is limited and the analysis methods used vary in terms of methodology, treated pathology, and follow-up duration. To successfully integrate computational simulations and engineering evaluations of medical images into clinical practice, it is crucial to standardize the analysis 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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".