Clot perviousness is associated with first pass success of aspiration thrombectomy in the COMPASS trial
Bibliographic record
Abstract
BACKGROUND: Clot density (Hounsfield units, HU) and perviousness (post-contrast increase in the HU of clot) are thought to be associated with clot composition. We evaluate whether these imaging characteristics were associated with angiographic outcomes of aspiration and stent retriever thrombectomy in COMPASS: a trial of aspiration thrombectomy versus stent retriever thrombectomy as first-line approach for large vessel occlusion. METHODS: Clot density and perviousness were measured by two independent operators who were blind to all the final angiographic and clinical outcomes. The association of clot density and perviousness with the Thrombolysis In Cerebral Infarction (TICI) scale after first pass was assessed using univariate and multivariate analysis. RESULTS: Among all patients enrolled in COMPASS, 165 were eligible for the post-hoc analysis (81 patients in the aspiration first and 84 in the stent retriever first groups). Overall mean perviousness of clot was significantly higher in patient with mTICI 2b-3 after first pass (28.6±22.9 vs 20.3±19.2, p=0.017). Mean perviousness among patients who achieved TICI 2c/3 versus TICI 2b versus TICI 0-2a in the aspiration first group varied significantly (32.6±26.1, 35.3±24.4, and 17.7±13.1, p=0.013). The association of perviousness with first pass success was not significant in the stent retriever group. Using multivariate analysis, high perviousness (defined as cut-off >27.6) was an independent predictor of TICI 2b-3 (OR 3.82, 95% CI 1.10 to 13.19; p=0.034). CONCLUSIONS: Clot perviousness is associated with first pass angiographic success in patients treated with the aspiration first approach for thrombectomy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".