Per pass analysis of thrombus composition retrieved by mechanical thrombectomy
Bibliographic record
Abstract
BACKGROUND AND AIM: Mechanical thrombectomy (MT) for large vessel occlusion often requires multiple passes to retrieve the entire thrombus load. In this multi-institutional study we sought to examine the composition of thrombus fragments retrieved with each pass during MT. METHODS: Patients who required multiple passes during thrombectomy were included. Histopathological evaluation of thrombus fragments retrieved from each pass was performed using Martius Scarlet Blue staining and the composition of each thrombus component including RBC, fibrin and platelet was determined using image analysis software. RESULTS: 154 patients underwent MT and 868 passes was performed which resulted in 263 thrombus fragments retrieval. The analysis of thrombus components per pass showed higher RBC, lower fibrin and platelet composition in the pass 1 and 2 when compared to pass 3 and passes 4 or more combined (P values <0.05). There were no significant differences between thrombus fragments retrieved in pass 1 and pass 2 in terms of RBC, WBC, fibrin, and platelet composition (P values >0.05). Similarly, when each composition of thrombus fragments retrieved in pass 3 and passes 4 or more combined were compared with each other, no significant difference was noted (P values >0.05). CONCLUSION: Our findings confirm that thrombus fragments retrieved with each pass differed significantly in histological content. Fragments in the first passes were associated with lower fibrin and platelet composition compared to fragments retrieved in passes three and four or higher. Also, thrombus fragments retrieved after failed pass were associated with higher fibrin and platelet components.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".