Prediction of parenchymal hematoma after mechanical thrombectomy by asymmetrical prominent veins: a retrospective cohort study based on susceptibility-weighted imaging
Bibliographic record
Abstract
Background: Asymmetrical prominent veins (APVs) can help predict perfusion deficits and collateral circulation in large vessel occlusion acute ischemic stroke. Therefore, this study aimed to investigate the predictive value of APVs for parenchymal hematoma (PH) following mechanical thrombectomy (MT). Methods: This study retrospectively included consecutive patients with ischemic stroke due to middle cerebral artery occlusion who underwent MT. APVs were quantified using the APVs-Alberta Stroke Program Early Computed Tomography Score (ASPECTS) system, and we recorded the length and diameter of the susceptibility vessel sign (SVS), the number of cerebral microbleeds (CMBs), post-MT blood flow grading, and whether PH occurred within 24 hours. Logistic regression was performed to identify risk factors for PH. Results: A total of 89 patients with acute middle cerebral artery occlusion were included. APVs-ASPECTS was identified as an independent predictor of PH [odds ratio (OR) =0.604 (0.392, 0.930); P=0.022], and the length of SVS [OR =0.882 (0.792, 0.981); P=0.021] was an independent predictor of successful recanalization [modified Thrombolysis In Cerebral Ischemia (mTICI) 2b/3] after MT. There was no significant difference in the number of CMBs between the PH (+) and PH (-) groups (P=0.341). Conclusions: In patients with acute middle cerebral artery occlusion who underwent MT, APVs-ASPECTS may be associated with the risk of PH.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".