Local Diameter, Wall Stress, and Thrombus Thickness Influence the Local Growth of Abdominal Aortic Aneurysms
Bibliographic record
Abstract
PURPOSE: To investigate the influence of the local diameter, the intraluminal thrombus (ILT) thickness, and wall stress on the local growth rate of abdominal aortic aneurysms. METHODS: The infrarenal aortas of 90 asymptomatic abdominal aortic aneurysm (AAA) patients (mean age 70 years; 77 men) were retrospectively reconstructed from at least 2 computed tomography angiography scans (median follow-up of 1 year) and biomechanically analyzed with the finite element method. Each individual AAA model was automatically sliced orthogonally to the lumen centerline and represented by 100 cross sections with corresponding diameters, ILT thicknesses, and wall stresses. The data were grouped according to these parameters for comparison of differences among the variables. RESULTS: Diameter growth was continuously distributed over the entire aneurysm sac, reaching absolute and relative median peaks of 3.06 mm/y and 7.3%/y, respectively. The local growth rate was dependent on the local baseline diameter, the local ILT thickness, and for wall segments not covered by ILT, also on the local wall stress level (all p<0.001). For wall segments that were covered by a thick ILT layer, wall stress did not affect the growth rate (p=0.08). CONCLUSION: Diameter is not only a strong global predictor but also a local predictor of aneurysm growth. In addition, and independent of the diameter, the ILT thickness and wall stress (for the ILT-free wall) also influence the local growth rate. The high stress sensitivity of nondilated aortic walls suggests that wall stress peaks could initiate AAA formation. In contrast, local diameters and ILT thicknesses determine AAA growth for dilated and ILT-covered aortic walls.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".