Per pass analysis of thrombus composition retrieved by mechanical thrombectomy
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 it