Thrombus Composition, Imaging, and Outcome Prediction in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: New imaging techniques have advanced our ability to capture thrombus characteristics and burden in real time. An improved understanding of recanalization rates with thrombolysis and endovascular thrombectomy based on thrombus characteristics has spurred interest in new therapies for acute stroke. METHODS AND RESULTS: This article reviews the biochemical, structural, and imaging characteristics of intracranial thrombi in acute ischemic stroke; the relationship between thrombus composition and response to lytic and endovascular therapies; and current and future directions for improving outcomes in patients with acute stroke based on thrombus characteristics. DISCUSSION: Thrombus composition, size, location, and timing from stroke onset correlate with imaging findings in acute ischemic stroke and are associated with clinical outcome. Further research across multiple domains could assist in better applying our knowledge of thrombi to patient selection and individualization of acute therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".