Preclinical Direct Endoluminal Assessment of Endothelialization After Flow Diversion With Microangioscopy
Bibliographic record
Abstract
Background Direct endoluminal imaging, such as with our previously described microangioscope, is an emerging adjunct in endovascular cerebral aneurysm management that enables clinicians to delineate between thrombi and visualize neoepithelialization after stent placement with high resolution. The present study sought to study flow diversion in vivo under direct endoluminal imaging, and to validate our findings with histopathology. Methods In a rabbit model, we implanted each left common carotid artery with a shielded flow diverter (FD) (Medtronic Pipeline Vantage) and nonshielded FD (Medtronic Pipeline Flex) in the right common carotid artery. We studied 9 animals in 3 groups: (1) no periprocedural antiplatelet therapy, (2) aspirin 81 mg daily, and (3) aspirin 81 mg and clopidogrel 75 mg daily. FD thrombosis, stenosis, malapposition, and neoepithelialization were all evaluated by diagnostic cerebral angiography and microangioscopy after 30 days. Diagnostic cerebral angiography and angioscopic video were analyzed by independent evaluators and compared with histopathologic analysis. Results In the aspirin and dual antiplatelet therapy groups, there were no significant observed differences in stent thrombosis, stenosis, malapposition, or neoepithelialization between the shielded and non‐shielded FD groups. There was significantly more thrombus formation in Group 1. Neointimal thickness as measured by the microangioscope was highly correlated with histology (r = 0.72; P = 0.016). Interrater agreement of microangioscope videos was highest for FD thrombosis and stenosis. Conclusion In‐stent thrombosis, stenosis, malapposition, and neopithelialization demonstrated no significant difference between shielded and non‐shielded FDs. Microangioscopy measurements for neointimal thickness were highly correlated with pathology and may be a helpful adjunct to diagnostic cerebral angiography in FD follow‐up.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".