Investigating an area of reduced wall thickness as a mechanism of Abdominal Aortic Aneurysm initiation
Bibliographic record
Abstract
Few previous studies of Abdominal Aortic Aneurysms (AAAs) have incorporated empirical measurements to compare against their corresponding computational material models. Even fewer studies have been conducted to investigate causes of AAAs. The research objective of this thesis is to investigate an area of reduced wall thickness as a factor in aneurysm initiation, using a “tissue-like” material and comparing with the material model most commonly used for aortic simulations. A “tissue-like” silicone material, Smooth-Sil 940, was selected and a study was completed to obtain the characteristics of the material. The material was then utilized to create specimens for a physical experimental model, and its material properties were utilized to generate computational models with the same material characteristics and geometry of the experimental models. Two studies were then completed in parallel; one computational and one experimental. When both studies were completed the results were compared and observations were made regarding the validity of the computational models, and the impact of an area of reduced wall thickness. Results from both studies suggest that an area of reduced wall thickness could be a critical factor for aneurysm initiation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".