A multicenter trial on the predictors of different subtypes of hemorrhagic infarction after thrombolysis
Bibliographic record
Abstract
Worldwide, stroke is a leading cause of long-term disability in adults. Alteplase is the only approved treatment for acute ischemic stroke (AIS) and results in an improvement in a third of treated patients. Most studies evaluated the post-alteplase haemorrhagic transformation of brain infarction as a homogeneous entity but we evaluated the predictors of each subtype of haemorrhagic transformation of brain infarction. Our trial included 616 AIS alteplase-treated patients. We evaluated the ability of different risk factors, clinical presentation, and imaging features to predict different haemorrhagic transformation (HT) subtypes. HT was seen in 152 patients (24.7%), higher NIHSS, cardioembolic stroke and atrial fibrillation were independent predictors of all ECASS-based subtypes of hemorrhagic infarction, in addition, anterior-circulation stroke was an independent predictor of hemorrhagic infarction type 1 (odds ratio [OR], 11.04; 95% CI, 9.81 to 12.70; P-value > 0.001) and type2 (OR, 11.89; 95% CI, 9.79 to 14.44; P-value > 0.001), while older age was also an independent predictor of parenchymal hematoma type1 (OR, 1.312; 95% CI, 1.245 to 1.912; P-value 0.02). In AIS patients treated with alteplase in Egypt and Saudi Arabia, higher NIHSS, cardioembolic stroke and atrial fibrillation were independent predictors of all ECASS-based subtypes of hemorrhagic infarction; in addition, anterior-circulation stroke was an independent predictor of hemorrhagic infarction type 1 and 2, while older age was also an independent predictor of parenchymal hematoma type1. Trial registration: (clinicaltrials.gov NCT06337175), retrospectively registered on 29/03/2024.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".