Edaravone combined with recombinant tissue plasminogen activator for the treatment of acute ischemic stroke: a meta-analysis
Bibliographic record
Abstract
Objective To evaluate the effectiveness and safety of edaravone combined with recombinant tissue plasminogen activator for the treatment of acute ischemic stroke. Methods The Cochrane Central Register of Controlled Trials (CENTRA), EMbase, PubMed, China Biology Medicine disc (CBMdisc), Wanfang Data, and VIP Information System were retrieved with a computer. The randomized controlled trial (RCT) and cohort study (up to December 2015) about edaravone combined with rtPA intravenous thrombolysis for the treatment of acute ischemic stroke were selected. Two reviewers independently screened the literature according to the inclusion and exclusion criteria. They extracted data and evaluated the methodological quality included in the studies. The modified Jadad quality scale was used to evaluate RCT, and 4-7 were high quality. The Newcastle-Ottawa scale was used to evaluate the cohort studies, and 5-9 stars were high quality. RevMan 5.2 software was used to conduct Meta-analysis for the high quality studies. Results A total of 881 patients in 1 RCT and 2 cohort studies were enrolled, including 367 patients treated with rtPA + edaravone (test group) and 474 treated with rtPA only (control group). Meta-analysis showed that clinical outcome of the test group was significantly superior to that of the control group (relative risk 1.28, 95% confidence interval 1.03-1.60; P=0.02), and it did not significantly increase the incidence of intracranial hemorrhage (relative risk 1.49, 95% confidence interval 0.71-3.14; P=0.08). Conclusions Edaravone combined with rtPA intravenous thrombolytic therapy may significantly improve the clinical outcome of patients with acute ischemic stroke, and the adverse reactions are less. Key words: Stroke; Brain Ischemia; Tissue Plasminogen Activator; Free Radical Scavengers; Treatment Outcome; Meta-Analysis
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".