Abstract 1: Optical Coherence Tomography Imaging in Acute Ischemic Stroke: Preliminary Animal and Human Results
Bibliographic record
Abstract
Background: Studies evaluating endothelial injury after EVT have been done by means of retrieved human thrombus, MR vessel-wall imaging, and animal histopathologic studies. These techniques have limitations, as MR imaging has insufficient spatial resolution to directly visualize endothelium, and histopathologic examinations are ex-vivo and unable to provide real-time patterns of injury. Objective: Endovascular imaging after EVT using optical coherence tomography (OCT) to examine for vessel injury in real-time. Methodology: Three swine weighing 35-40kg were selected for the animal model. Autologous venous whole-blood was used to create thrombus. A second-generation stent retriever was used for EVT. Next, three consecutive patients with basilar artery occlusion underwent EVT and endovascular OCT imaging. Results: In the animal model, revascularization and OCT imaging was successful for all 9 vessels. Endothelial injury was observed in 4/9 (44%) of vessels, and residual thrombus was observed in 4/9 (44%) of vessels despite complete angiographic revascularization. All vessels undergoing EVT after 6 hours had evidence of endothelial injury, and 2/3 (66%) had residual thrombus. Two basilar stroke patients (2/3) 66% had significant residual thrombus despite complete angiographic revascularization. The residual thrombus was also not visible on CT angiography or MR imaging done within 24 hours of EVT. Conclusions: Endothelial injury and residual thrombus despite complete revascularization is present after EVT and can be observed in real-time using OCT. It is possible that the longer occlusive thrombus is present, the more endothelial injury will occur during EVT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".