Direct endovascular thrombectomy and bridging strategies for acute ischemic stroke: a network meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The present Bayesian network meta-analysis aimed to compare the various strategies for acute ischemic stroke: direct endovascular thrombectomy within the thrombolysis window in patients with no contraindications to thrombolysis (DEVT); (2) direct endovascular thrombectomy secondary to contraindications to thrombolysis (DEVTc); (3) endovascular thrombectomy in addition to thrombolysis (IVEVT); and (4) thrombolysis without thrombectomy (IVT). METHODS: Six electronic databases were searched from their dates of inception to May 2017 to identify randomized controlled trials (RCTs) comparing IVT versus IVEVT, and prospective registry studies comparing IVEVT versus DEVT or IVEVT versus DEVTc. Network meta-analyses were performed using ORs and 95% CIs as the summary statistic. RESULTS: We identified 12 studies (5 RCTs, 7 prospective cohort) with a total of 3161 patients for analysis. There was no significant difference in good functional outcome at 90 days (modified Rankin Scale score ≤2) between DEVT and IVEVT. There was no significant difference in mortality between all treatment groups. DEVT was associated with a 49% reduction in intracranial hemorrhage (ICH) compared with IVEVT (OR 0.51; 95% CI 0.33 to 0.79), due to reduction in rates of asymptomatic ICH (OR 0.47; 95% CI 0.29 to 0.76). Patients treated with DEVT had higher rates of reperfusion compared with IVEVT (OR 1.73; 95% CI 1.04 to 2.94). CONCLUSIONS: To our knowledge, this is the first network meta-analysis to be performed in the era of contemporary mechanical thrombectomy comparing DEVT and DEVTc. Our analysis suggests the addition of thrombolysis prior to thrombectomy for large vessel occlusions may not be associated with improved outcomes.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.051 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".