Is Histologic Thrombus Composition in Acute Stroke Linked to Stroke Etiology or to Interventional Parameters?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Detailed insight into the composition of thrombi retrieved from patients with ischemic stroke by mechanical thrombectomy might improve pathophysiologic understanding and therapy. Thus, this study searched for links between histologic thrombus composition and stroke subtypes and mechanical thrombectomy results. MATERIALS AND METHODS: Thrombi from 85 patients who had undergone mechanical thrombectomy for acute ischemic stroke between December 2016 and March 2018 were studied retrospectively. Thrombi were examined histologically. Preinterventional imaging features, stroke subtypes, and interventional parameters were re-analyzed. Statistical analysis was performed with the Kruskal-Wallis test, Mann-Whitney U test, or Spearman correlation as appropriate. RESULTS: Cardioembolic thrombi had a higher percentage of macrophages and a tendency toward more platelets than thrombi of large-artery atherosclerotic stenosis (P = .021 and .003) or the embolic stroke of undetermined source (P = .037 and .099) subtype. Thrombi prone to fragmentation required the combined use of contact aspiration and stent retrieval (P = .021) and were associated with an increased number of retrieving maneuvers (P = .001), longer procedural times (P = .001), and a higher lymphocyte content (P = .035). CONCLUSIONS: We interpreted the higher macrophage and platelet content in cardioembolic thrombi compared with large-artery atherosclerotic stenosis or embolic stroke of undetermined source thrombi as an indication that the latter type might be derived from an atherosclerotic plaque rather than from an undetermined cardiac source. The extent of thrombus fragmentation was associated with a more challenging mechanical thrombectomy and a higher lymphocyte content of the thrombi. Thus, thrombus fragmentation not only might be caused by the recanalization procedure but also might be a feature of a lymphocyte-rich, difficult-to-retrieve subgroup of thrombi.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".