Intra‐Arterial Thrombolysis After Successful Thrombectomy: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: This study aims to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) to assess the efficacy and safety of intra-arterial thrombolysis (IAT) following successful endovascular thrombectomy (EVT) in patients with stroke. Methods: A systematic literature search was conducted to identify RCTs comparing IAT versus no IAT after successful EVT. The primary efficacy outcome was a modified Rankin Scale score of 0-1 at 90 days, and the primary safety outcomes included symptomatic intracranial hemorrhage and 90-day mortality. Subgroup meta-analyses were conducted based on expanded Thrombolysis in Cerebral Infarction (eTICI) and prior intravenous thrombolysis (IVT). Both random-effects and common-effect models were applied with model selection determined by the level of heterogeneity. Results: = 0.31). Additionally, IAT treatment did not increase the risk of symptomatic intracranial hemorrhage (RR: 1.14 [95% CI: 0.85-1.54]) or 90-day mortality (RR: 1.05 [95% CI: 0.87-1.26]). Subgroup meta-analysis suggested greater benefits from IAT in patients with eTICI 2b50/67 (RR: 1.51 [95% CI: 1.03-2.23]) than in those with eTICI 2c/3 (RR: 1.22, 95% CI: 0.99-1.50), and in patients without prior IVT (RR: 1.33 [95% CI: 1.08-1.65]) compared with those who received IVT (RR: 1.17 [95% CI: 0.85-1.62]). Conclusion: IAT following successful EVT improved 90-day functional outcomes without increasing the risk of symptomatic intracranial hemorrhage or 90-day mortality. Patients in the eTICI 2b50/67 subgroup and those without prior IVT showed a trend toward greater benefit from IAT compared with the eTICI 2c/3 subgroup and those who received IVT prior to thrombectomy.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".