Biomarkers of Endothelial Activation in Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage: A Prospective Cohort Study
Bibliographic record
Abstract
ABSTRACT Background: Endothelial cell activation seems to be an important process in the multifactorial pathophysiology of delayed cerebral ischemia (DCI) and subsequent poor clinical outcome after aneurysmal subarachnoid hemorrhage (aSAH). Aim: To assess the association between biomarker levels of endothelial activation and the occurrence of DCI and poor clinical outcome six months after aSAH. Methods: Between October 2018 and November 2020, 75 aSAH patients were included. Blood samples were taken on admission, days 3–5 and days 9–11 after aSAH. Ten patients with unruptured intracranial aneurysms served as controls. Poor outcome was assessed at six months, defined by a modified Rankin Scale score of 4–6. The cohort was dichotomized into patients with and without DCI and good and poor outcomes. Biomarker levels of von Willebrand factor (vWF), E-selectin, thrombomodulin, syndecan-1 and matrix metalloproteinase (MMP-9) were analyzed and compared between groups by a T -test or Mann–Whitney U test, depending on the normality of the data. Results: Twelve (16.0%) patients developed DCI, and 39 (41.9%) patients had poor outcomes at six months post-aSAH. None of the biomarkers showed significant differences between patients with and without DCI. vWF and syndecan-1 were elevated on admission and on days 9–11 in patients with poor outcomes ( p < 0.05 and p = 0.02, respectively). Conclusion: Levels of vWF, E-selectin, thrombomodulin, syndecan-1 and MMP-9 were not associated with the occurrence of DCI, although higher levels of vWF and syndecan-1 were associated with poor outcome at six months. Further research is needed to establish the role of these biomarkers in aSAH patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".