Abstract 99: Clot Composition And Reperfusion Outcomes In 1430 Mechanical Thrombectomy Patients: Analysis Of The Stroke Thromboembolism Registry Of Imaging And Pathology
Bibliographic record
Abstract
Background: Understanding clot composition may allow for better technical planning and improved outcomes in mechanical thrombectomy (MT). We sought to correlate clot composition with reperfusion outcomes in MT. 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 and eosin and Martius Scarlett Blue. Percent composition, richness, and gross appearance were evaluated. Outcome measures included the rate of First-Pass Effect (mTICI2c/3, FPE) and the number of device passes. Results: A total of 1430 patients (mean age, 68.4±13.5years; median [IQR] baseline NIHSS,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 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 compared to RBC-rich and mixed clots (median, 2 and 1.5 vs.1, respectively, P=0.02). CA displayed 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. Conclusion: Despite the lack of correlation between clot histology and FPE in the overall population, our study adds to the growing body of evidence supporting the notion that clot composition influences reperfusion treatment strategy outcomes. Additional studies are needed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".