Thrombosis Heralding Aneurysmal Rupture: An Exploration of Potential Mechanisms in a Novel Giant Swine Aneurysm Model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The relationship between aneurysm dimensions, flow, thrombosis, and rupture remains poorly understood. We attempted to clarify this relationship by exploring various swine aneurysm models. MATERIALS AND METHODS: Bilateral carotid aneurysms were constructed according to 3 protocols in 24 animals: small aneurysms with wide necks (group 1; n = 6 animals); small aneurysms with small necks (group 2; n = 4 animals), and giant aneurysms with large necks (group 3; n = 14 animals). Group 3 included 3 subgroups, related to testing the model in various experimental conditions: The neck was clipped in 3 animals; venous pouches lacked an endothelial lining in 4 animals; and 7 were control animals. Animals were followed until rupture, or for 1-4 weeks. Angiography was performed postoperatively and before euthanasia. We studied lesion pathology, paying attention to thrombosis, recanalization, wall composition, and perianeurysmal hemorrhage. RESULTS: Groups differed significantly in aneurysm dimensions and aspect ratio (P = .002). Ruptures occurred more frequently in animals with untreated giant aneurysms (7/7) than in animals with small wide-neck (0/6) or small-neck (2/4) aneurysms (P = .002). Ruptures occurred only in animals with thrombosed aneurysms. Lesions lacking an endothelial lining and 5 of 6 clipped venous pouches thrombosed but did not rupture. One giant lesion ruptured despite complete clipping. The wall was deficient in α-actin and was infiltrated with inflammatory cells and erythrocytes in all thrombosed cases, ruptured or not. Ruptures were associated with recanalizing channels in 9 of 10 cases. CONCLUSIONS: Thrombosis, inflammation, and recanalization may precipitate aneurysmal ruptures in a swine model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".