Edaravone dexborneol for the treatment of acute ischemic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Edaravone dexborneol has been developed as a novel neuroprotective agent and showed a promising result in treatment of stroke. The current meta-analysis aimed to assess the feasibility and efficacy of the edaravone dexborneol in the treatment of stroke. Method We performed a systematic review and meta-analysis of literature in four electronic databases. Binary outcomes were analyzed through the risks ratio (RR) and 95% confidence interval (CI), while the continuous outcomes were analyzed through the standardized mean difference (SMD) and 95% CI. Also, we did a subgroup analysis to show more feasibility and safety dimensions. Results Five studies with a total of 2415 patients were included. There were 1119 patients in edaravone dexborneol group and 1216 patients in control group. The 90-mRS 0–1 (RR 1.17 [95% CI 1.09–1.25]; p < 0.0001) and 90-day mRS 0–2 (RR 1.12 [95% CI 1.07–1.18]; p < 0.0001) were statistically significant higher in intervention group compared with control group. There was no significant difference between intervention group and control group concerning 90-day mRS 0–3 (RR 1.03 [95% CI 0.99–1.06]; p = 0.07), 90-day mortality rate (RR 0.71 [95% CI 0.45–1.11]; p = 0.13), serious adverse events (RR 0.91 [95% CI 0.72–1.16]; p = 0.45), and NIHSS score ≤1 at days 14 (RR 0.96; p = 0.69), 30 (RR 1.08; p = 0.18), and 90 (RR 1.06; p = 0.15). No heterogeneity in treatment effect was seen in the analysis, and any potential discrepancies were addressed by sensitivity analysis. Conclusion Edaravone dexborneol can be a favorable treatment option for patients with stroke. However, more randomized controlled trials are required to confirm our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".