A cross-sectional study on the correlation between internal cerebral vein asymmetry and hemorrhagic transformation following endovascular thrombectomy
Bibliographic record
Abstract
Introduction: Hemorrhagic transformation (HT) is a severe complication in patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO) after endovascular treatment (EVT). We hypothesize that asymmetry of the internal cerebral veins (ICVs) on baseline CT angiogram (CTA) may serve as an adjunctive predictor of HT. Methods: We conducted a study on consecutive AIS-LVO patients from November 2020 to April 2022. These patients had anterior circulation occlusions and were treated with EVT. Asymmetrical ICVs were assessed using CTA and defined as hypodensity (reduced opacification) on the ipsilateral side of occlusion compared to the contralateral side. The primary outcome was HT, defined as hemorrhage within the ischemic territory. This was evaluated using follow-up imaging (CT scan or magnetic resonance imaging) performed 48 h post-EVT. HT was classified into four subtypes based on the European Cooperative Acute Stroke Study-II criteria. Results: = 0.004). In multivariate logistic regression, ICV asymmetry (OR 3.809, 95% CI 1.582-9.171), baseline Alberta Stroke Program Early CT Score (OR 0.771, 95% CI 0.608-0.978), intravenous recombinant tissue plasminogen activator (OR 2.847, 95% CI 1.098-2.7.385), and poor collateral circulation (OR 3.998, 95% CI 1.572-10.169) were identified as independent risk factors of HT. Conclusion: ICV asymmetry, likely resulting from impaired autoregulation or tissue micro-perfusion hampering cerebral blood flow (CBF), is a novel radiological sign that independently predicts HT. It is associated with a higher risk of sICH in AIS-LVO patients after EVT. Further research is warranted to validate these findings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".