GP.3 Examining Aneurysmal Healing After Flow Diversion Treatment Using Endovascular Optical Coherence Tomography
Bibliographic record
Abstract
Background: The mechanism of aneurysmal healing after flow-diversion treatment of cerebral aneurysms remains unknown. The purpose of this research to is to utilize a novel technology called endovascular optical coherence tomography (OCT) to characterise and improve our understanding of aneurysmal healing after flow-diversion using a rabbit aneurysm model. Methods: Saccular aneurysms were created in 10 New Zealand white rabbits. The aneurysms were treated with a flow-diverting stent 28 days after creation. OCT and histopathologic examinations included: luminal thrombosis, endothelial loss, inflammation, fibrin, smooth muscle cell loss, disruption of the internal and external elastic lamina, and tunica adventitia changes Results: OCT revealed endothelialization across the stent, appearing to originate from the parent vessel, along with small amounts of thrombus on the stent-struts. Minimal thrombus was visualized within the aneurysm sac. Histologic examination revealed that OCT can accurately define endothelialization across the sent, and define patent segments across the neck. Conclusions: Aneurysmal healing appears to originate at the parent vessel/stent interface, and use the stent as a scaffold to grow across the neck of the aneurysm. Minimal thrombus was visualized within the aneurysm sac, with ongoing flow observed in the setting of incomplete neck endothelialization. This technology has great potential for assessing aneurysmal healing in real-time.
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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) | 0.000 | 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) | 0.002 | 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".