Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Hemorrhagic transformation (HT) following reperfusion therapies for acute ischaemic stroke often predicts a poor prognosis. This systematic review and meta-analysis aims to identify risk factors for HT, and how these vary with hyperacute treatment [intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)]. Methods Electronic databases PubMed and EMBASE were used to search relevant studies. Pooled odds ratio (OR) with 95% confidence interval (CI) were estimated. Results A total of 120 studies were included. Atrial fibrillation and NIHSS score were common predictors for any intracerebral hemorrhage (ICH) after reperfusion therapies (both IVT and EVT), while a hyperdense artery sign (OR = 2.605, 95% CI 1.212–5.599, I2 = 0.0%) and number of thrombectomy passes (OR = 1.151, 95% CI 1.041–1.272, I2 = 54.3%) were predictors of any ICH after IVT and EVT, respectively. Common predictors for symptomatic ICH (sICH) after reperfusion therapies were age and serum glucose level. Atrial fibrillation (OR = 3.867, 95% CI 1.970–7.591, I2 = 29.1%), NIHSS score (OR = 1.082, 95% CI 1.060–1.105, I2 = 54.5%) and onset-to-treatment time (OR = 1.003, 95% CI 1.001–1.005, I2 = 0.0%) were predictors of sICH after IVT. Alberta Stroke Program Early CT score (ASPECTS) (OR = 0.686, 95% CI 0.565–0.833, I2 =77.6%) and number of thrombectomy passes (OR = 1.374, 95% CI 1.012–1.866, I2 = 86.4%) were predictors of sICH after EVT. Conclusion Several predictors of ICH were identified, which varied by treatment type. Studies based on larger and multi-center data sets should be prioritized to confirm the results. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=268927 , identifier: CRD42021268927.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".