Increased Perviousness on CT for Acute Ischemic Stroke is Associated with Fibrin/Platelet-Rich Clots
Bibliographic record
Abstract
<h3>BACKGROUND AND PURPOSE:</h3> Clot perviousness in acute ischemic stroke is a potential CT imaging biomarker for mechanical thrombectomy efficacy. We investigated the association among perviousness, clot cellular composition, and first-pass effect. <h3>MATERIALS AND METHODS:</h3> In 40 mechanical thrombectomy–treated cases of acute ischemic stroke, we calculated perviousness as the difference in clot density on CT angiography and noncontrast CT. We assessed the proportion of fibrin/platelet aggregates, red blood cells, and white blood cells on clot histopathology. We tested for linear correlation between histologic components and perviousness, differences in components between “high” and “low” pervious clots defined by median perviousness, and differences in perviousness/composition between cases that did and did not achieve a first-pass effect. <h3>RESULTS:</h3> Perviousness significantly positively and negatively correlated with the percentage of fibrin/platelet aggregates (<i>P </i>= .001) and the percentage of red blood cells (<i>P </i>= .001), respectively. Higher pervious clots had significantly greater fibrin/platelet aggregate content (<i>P </i>= .042). Cases that achieved a first-pass effect (<i>n</i> = 14) had lower perviousness, though not significantly (<i>P</i> = .055). The percentage of red blood cells was significantly higher (<i>P </i>= .028) and the percentage of fibrin/platelet aggregates was significantly lower (<i>P </i>= .016) in cases with a first-pass effect. There was no association between clot density on NCCT and clot composition or first-pass effect. Receiver operating characteristic analysis indicated that clot composition was the best predictor of first-pass effect (area under receiver operating characteristic curve: percentage of fibrin/platelet aggregates = 0.731, percentage of red blood cells = 0.706, perviousness = 0.668). <h3>CONCLUSIONS:</h3> Clot perviousness on CT is associated with a higher percentage of fibrin/platelet aggregate content. Histologic data and, to a lesser degree, perviousness may have value in predicting first-pass outcome. Imaging metrics that more strongly reflect clot biology than perviousness may be needed to predict a first-pass effect with high accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".