Inflammation and the absence of edema in the abdominal aortic aneurysm as determined by T2-weighted cardiovascular magnetic resonance imaging
Bibliographic record
Abstract
Objective: The non-specific abdominal aortic aneurysm (AAA) is a local manifestation of a systemic disease, in which inflammation may play a role. This cardiovascular magnetic resonance (CMR) study utilizes a water-sensitive, T2- weighted, short tau inversion recovery sequence (T2-STIR) to identify vessel wall edema as a marker for inflammation. Methods: Twenty-two patients were included: 10 AAA patients, 10 healthy subjects, and two patients with known inflammation. MR T2-STIR images of aorta vessel wall and intraluminal thrombi were analyzed using OSIRIX software. Signal intensity values were normalized, and values from blinded and independent viewers were then averaged and analyzed using SPSS statistical software. The Kruskal-Wallis H test was used with post hoc analysis for differences of significance. Results: Average AAA anterior-posterior diameter was 5.9 ± 0.6cm (range, 5.3-7.0cm). The Kruskal-Wallis H test revealed a significant difference between independent samples (H(3) = 20.36, P < .001). There was no significant difference in average intensities between AAA and healthy subjects ( P = .766). Conclusion: This is the first study to examine edema in walls and thrombi of AAAs using T2-STIR imaging. No evidence of edema was identified in the aortic aneurysm wall, suggesting a lack of inflammatory activity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".