Endovascular Thrombectomy With or Without Thrombolysis for Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: To this date, whether to administer intravenous thrombolysis (IVT) prior to endovascular thrombectomy (EVT) for stroke patients still stirs some debate. We aimed to systematically update the evidence from randomized trials comparing EVT alone vs EVT with bridging IVT. Methods: We searched MEDLINE, EMBASE, and the Cochrane Library to identify randomized controlled trials (RCTs) comparing EVT with or without IVT in patients presenting with stroke secondary to a large vessel occlusion. We conducted meta-analyses using random-effects models to compare functional independence, mortality, and symptomatic intracranial hemorrhage (sICH), between EVT and EVT with IVT. We assessed risk of bias using the Cochrane risk-of-bias tool and certainty of evidence for each outcome using the GRADE approach. Results: Of 11,111 citations, we included 6 studies with a total of 2336 participants. We found low-certainty evidence of possibly a small decrease in the proportion of patients with functional independence (risk difference [RD] -2.0%, 95% CI -5.9% to 2.0%), low-certainty evidence that there is possibly a small increase in mortality (RD 1.0%, 95% CI -2.2% to 4.7%), and moderate-certainty evidence that there is probably a decrease in sICH (RD -1.0%, 95% CI -1.6% to .7%) for patients with EVT alone compared to EVT plus IVT, respectively. Conclusion: Low-certainty evidence shows that there is possibly a small decrease in functional independence, low-certainty evidence shows that there is possibly a small increase in mortality, and moderate-certainty evidence that there is probably a decrease in sICH for patients with EVT alone compared to EVT plus IVT.
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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 | 0.030 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.044 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".