Abstract 42: Chronic Endothelial Activation in Childhood Arterial Ischemic Stroke
Bibliographic record
Abstract
Objective: Despite evidence suggesting that arterial inflammation plays a role in the pathophysiology of childhood arterial ischemic stroke (AIS), relatively little is known about inflammatory induced coagulation and/or endothelial activation. The object of this study was to compare biomarkers of coagulation activation [D-dimer and thrombin-antithrombin complex (TAT)] and endothelial activation [Plasminogen activator inhibitor-1 (PAI-1) and von Willebrand factor antigen (vWF Ag)] in children with AIS as compared to healthy pediatric controls. Methods: Sixty patients with childhood AIS (ages 28 days to 19 years) were enrolled in a prospective six-center study. Biomarker samples were collected in the acute (0-3 weeks post-AIS) and chronic (> 3 months post-AIS) timeframes. Healthy pediatric controls were enrolled at the central site. Biomarker differences by group were examined using t-test, chi-square, and, if demographics differed, regression models. Results: Age and gender were similar in all case and control groups, except age in acute vWF Ag and D-dimer populations [mean (SD): vWF Ag age = 9.3 yrs. (5.2), control age = 6.9 yrs. (4.3), P=0.007 ; D-Dimer age = 9.4 yrs. (5.2), control age = 7.3 yrs. (4.3), P=0.032 ]. All acute biomarkers were significantly elevated in cases as compared to healthy controls, while only markers of endothelial activation remained significantly elevated in cases during the chronic phase (PAI-1 P=0.0014 ; vWF Ag P=0.0002 ; Table). Conclusion: Children with AIS have increased levels of endothelial and coagulation activation during the acute phase following stroke, while endothelial biomarkers remain elevated beyond 3 months. These results suggest that endothelial activation persists into the chronic phase of childhood AIS. Future studies with larger cohorts and stroke subtype analysis are needed to evaluate the use of endothelial biomarkers as surrogates of ongoing disease and recurrence risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".