Blood–brain barrier leakage and hemorrhagic transformation: The Reperfusion Injury in Ischemic StroKe (RISK) study
Bibliographic record
Abstract
Abstract Background and purpose In patients with acute ischemic stroke treated with reperfusion therapy we aimed to evaluate whether pretreatment blood–brain barrier (BBB) leakage is associated with subsequent hemorrhagic transformation (HT). Methods We prospectively screened patients with acute ischemic stroke treated with intravenous thrombolysis and/or endovascular treatment. Before treatment, each patient received computed tomography (CT), CT angiography, and CT perfusion. We assessed pretreatment BBB leakage within the ischemic area using the volume transfer constant (Ktrans) value. Our primary outcome was relevant HT, defined as hemorrhagic infarction type 2 or parenchymal hemorrhage type 1 or 2. We evaluated independent associations between BBB leakage and HT using logistic regression, adjusting for age, sex, baseline stroke severity, Alberta Stroke Program Early CT Score (ASPECTS) ≥ 6, treatment type, and onset‐to‐treatment time. Results We enrolled 171 patients with available assessment of BBB leakage. The patients' mean (±SD) age was 75.5 (±11.8) years, 86 (50%) were men, and the median (interquartile range) National Institutes of Health Stroke Scale score was 18 (12–23). A total of 32 patients (18%) received intravenous thrombolysis, 102 (60%) underwent direct endovascular treatment, and 37 (22%) underwent both. Patients with relevant HT (N = 31;18%) had greater mean BBB leakage (Ktrans 0.77 vs. 0.60; p = 0.027). After adjustment in the logistic regression model, we found that BBB leakage was associated both with a more than twofold risk of relevant HT (odds ratio [OR] 2.50; 95% confidence interval [CI] 1.03–6.03 per Ktrans point increase; OR 2.34; 95% CI 1.06–5.17 for Ktrans values > 0.63 [mean BBB leakage value]) and with symptomatic intracerebral hemorrhage (OR 4.30; 95% CI 1.13–13.77 per Ktrans point increase). Conclusion Pretreatment BBB leakage before reperfusion therapy was associated with HT, and may help to identify patients at risk of HT.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".