Supplementary Material for: Endothelial dysfunction and parenchymal hematoma in ischemic stroke patients after endovascular thrombectomy
Bibliographic record
Abstract
Background and Purpose: Endothelial dysfunction (ED) may result in parenchymal injury and, therefore, worsen the outcomes of ischemic stroke. This study aimed to determine whether ED could predict parenchymal hematoma (PH) in ischemic stroke patients treated with endovascular thrombectomy (EVT). Methods: Patients with large artery occlusion in the anterior circulation and treated with EVT were prospectively enrolled from 2 stroke centers. Serum soluble intercellular adhesion molecule-1, soluble vascular cell adhesion molecule-1, soluble E-selectin, and von Willebrand factor (vWF) were tested and summed to a standardized score to reflect the levels of ED. PH was diagnosed according to the Heidelberg Bleeding Classification. Results: Of the 325 enrolled patients (mean age, 68.6 years; 207 men), 41 (12.6%) developed PH. Patients with PH had higher concentrations of soluble E-selectin, vWF, and ED sum score. After adjusting for demographic characteristics, National Institutes of Health Stroke Scale score, pre-treatment Alberta stroke program early computerized tomography score, and other potential confounders, the increased ED burden was associated with PH (odds ratio, 1.432; 95% confidence interval, 1.031−1.988; P=0.032). Similar significant results were found in the sensitivity analysis. The multiple-adjusted spline regression model showed a linear association between the total ED score and PH (P=0.001 for linearity). Adding ED score to the conventional model significantly improved the risk prediction of PH (net reclassification improvement=25.2%, P=0.001; integrated discrimination index=2.9%; P=0.001). Conclusions: This study demonstrated that ED might be related to PH. Introducing the ED score could increase the reliability of PH risk model for stroke patients treated with EVT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.797 | 0.191 |
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".