Clot composition and recanalization outcomes in mechanical thrombectomy
Bibliographic record
Abstract
BACKGROUND: Mechanical thrombectomy (MT) has become standard for large vessel occlusions, but rates of complete recanalization are suboptimal. Previous reports correlated radiographic signs with clot composition and a better response to specific techniques. Therefore, understanding clot composition may allow improved outcomes. METHODS: Clinical, imaging, and clot data from patients enrolled in the STRIP Registry from September 2016 to September 2020 were analyzed. Samples were fixed in 10% phosphate-buffered formalin and stained with hematoxylin-eosin and Martius Scarlett Blue. Percent composition, richness, and gross appearance were evaluated. Outcome measures included the rate of first-pass effect (FPE, modified Thrombolysis in Cerebral Infarction 2c/3) and the number of passes. RESULTS: A total of 1430 patients of mean±SD age 68.4±13.5 years (median (IQR) baseline National Institutes of Health Stroke Scale score 17.2 (10.5-23), IV-tPA use 36%, stent-retrievers (SR) 27%, contact aspiration (CA) 27%, combined SR+CA 43%) were included. The median (IQR) number of passes was 1 (1-2). FPE was achieved in 39.3% of the cases. There was no association between percent histological composition or clot richness and FPE in the overall population. However, the combined technique resulted in lower FPE rates for red blood cell (RBC)-rich (P<0.0001), platelet-rich (P=0.003), and mixed (P<0.0001) clots. Fibrin-rich and platelet-rich clots required a higher number of passes than RBC-rich and mixed clots (median 2 and 1.5 vs 1, respectively; P=0.02). CA showed a trend towards a higher number of passes with fibrin-rich clots (2 vs 1; P=0.12). By gross appearance, mixed/heterogeneous clots had lower FPE rates than red and white clots. CONCLUSIONS: Despite the lack of correlation between clot histology and FPE, our study adds to the growing evidence supporting the notion that clot composition influences recanalization treatment strategy outcomes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".