Clinical relevance of intracranial hemorrhage after thrombectomy versus medical management for large core infarct: a secondary analysis of the SELECT2 randomized trial
Bibliographic record
Abstract
Background The incidence of intracerebral hemorrhage (ICH) and its effect on the outcomes after endovascular thrombectomy (EVT) for patients with large core infarcts have not been well-characterized. Methods SELECT2 trial follow-up imaging was evaluated using the Heidelberg Bleeding Classification (HBC) to define hemorrhage grade. The association of ICH with clinical outcomes and treatment effect was examined. Results Of 351 included patients, 194 (55%) and 189 (54%) demonstrated intracranial and intracerebral hemorrhage, respectively, with a higher incidence in EVT (134 (75%) and 130 (73%)) versus medical management (MM) (60 (35%) and 59 (34%), both P<0.001). Hemorrhagic infarction type 1 (HBC=1a) and type 2 (HBC=1b) accounted for 93% of all hemorrhages. Parenchymal hematoma (PH) type 1 (HBC=1c) and type 2 (HBC=2) were observed in 1 (0.6%) EVT-treated and 4 (2.2%) MM patients. Symptomatic ICH (sICH) (SITS-MOST definition) was seen in 0.6% EVT patients and 1.2% MM patients. No trend for ICH with core volumes (P=0.10) or Alberta Stroke Program Early CT Score (ASPECTS) (P=0.74) was observed. Among EVT patients, the presence of any ICH did not worsen clinical outcome (modified Rankin Scale (mRS) at 90 days: 4 (3–6) vs 4 (3–6); adjusted generalized OR 1.00, 95% CI 0.68 to 1.47, P>0.99) or modify EVT treatment effect (Pinteraction=0.77). Conclusions ICH was present in 75% of the EVT population, but PH or sICH were infrequent. The presence of any ICH did not worsen functional outcomes or modify EVT treatment effect at 90-day follow-up. The high rate of hemorrhages overall still represents an opportunity for adjunctive therapies in EVT patients with a large ischemic core.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".