Analyses of thrombi in acute ischemic stroke: A consensus statement on current knowledge and future directions
Bibliographic record
Abstract
Limited data exist on clot composition and detailed characteristics of arterial thrombi associated with large vessel occlusion in acute ischemic stroke. Advances in endovascular thrombectomy and related imaging modalities have created a unique opportunity to analyze thrombi removed from cerebral arteries. Insights into thrombus composition, etiology, physical properties and neurovascular interactions may lead to future advancements in acute ischemic stroke treatment and improved clinical outcomes. Advances in imaging techniques may enhance clot characterization and inform therapeutic decision-making prior to treatment and reveal stroke etiology to guide secondary prevention. Current imaging techniques can provide some information about thrombi, but there remains much to evaluate about relationships that may exist among thrombus composition, occlusion characteristics and treatment outcomes. Improved pathophysiological characterization of clot types, their properties and how these properties change over time, together with clinical correlates from ongoing studies, may facilitate revascularization with thrombolysis and thrombectomy. Interdisciplinary approaches covering clinical, engineering and scientific aspects of thrombus research will be key to advancing the understanding of thrombi and improving acute ischemic stroke therapy. This consensus statement integrates recent research on clots and thrombi retrieved from cerebral arteries and provides a rationale for further analyses, including current opportunities and limitations.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".