Platelet-Rich Emboli in Cerebral Large Vessel Occlusion Are Associated With a Large Artery Atherosclerosis Source
Bibliographic record
Abstract
Background and Purpose- Nearly 30% of large vessel occlusion acute ischemic stroke clots are from an unknown source. We assessed histological clot composition in a series of patients with large vessel occlusion and investigated correlations between clot composition and stroke pathogenesis. Methods- As part of the multi-institutional STRIP registry (Stroke Thromboembolism Registry of Imaging and Pathology), consecutive emboli retrieved during mechanical thrombectomy were stained using Martius Scarlett Blue and analyzed using machine learning software. We assessed proportions of red blood cells, fibrin, platelets, and white blood cells. Correlations between clot components and stroke pathogenesis (large artery atherosclerosis, cardioembolism, and stroke of undetermined pathogenesis) were assessed using SPSS22. Results- One hundred five patients were included. The proportion of platelet-rich clots (55.0% versus 21.2%; P=0.005) and percentage of platelet content (22.1±4.2% versus 13.9±14.2%; P=0.03) was significantly higher in the large artery atherosclerosis group compared with the cardioembolic group. The proportion of platelet-rich clots (50.0% versus 21.2%; P=0.024) was also significantly higher in the cryptogenic group compared with cardioembolic cases. Large artery atherosclerosis and cryptogenic cases had a similar proportion of platelet-rich clots (55.0% versus 50.0%; P=0.636). There was no significant difference between stroke pathogenesis and the other major clot components. Conclusions- High platelet content of emboli is associated with a large artery atherosclerosis etiology of large vessel occlusion.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".