Endovascular thrombectomy for large-core stroke: a meta-analysis with trial sequential analysis
Bibliographic record
Abstract
Abstract Recent studies have reported that endovascular thrombectomy (ET) may improve neurological outcomes in large-core stroke. We performed a systematic review and meta-analysis to compare the pooled efficacy and safety of ET and of the best medical treatment among patients with large-core stroke. We searched the PubMed/MEDLINE, Scopus, and Cochrane databases from inception to November 2023. The inclusion criteria were randomized controlled trials (RCTs) comparing ET and the best medical treatment available among patients with large-core stroke (Alberta Stroke Program Early Computed Tomography Score [ASPECTS] < 6 or ischemic core > 50 mL on perfusion imaging) within 24 hours of symptom onset. We included 6 RTCs comprising 1,887 patients (ET group: n = 945). Endovascular thrombectomy was associated with good neurological outcomes (odds ratio [OR]: 2.92; 95% confidence interval [95%CI]: 2.17–3.93), or independent walking (OR: 2.22; 95%CI: 1.72–2.86). Trial sequential analysis confirmed a robust statistical significance for good neurological outcomes favoring ET. Endovascular thrombectomy was associated with higher risks of developing intracranial bleeding (OR: 2.65; 95%CI: 1.35–5.22) and symptomatic intracranial bleeding (OR: 1.83; 95%CI: 1.14–2.94). There were no differences between the groups regarding mortality or decompressive craniectomy. Patients submitted to non-contrast computed tomography (CT) with CT angiography (CTA) scans were analyzed separately and showed good neurological outcomes, comparable to those of the patients submitted to other imaging modalities (OR: 3.24; 95%CI: 1.52–6.92). Endovascular thrombectomy was associated with good neurological outcomes and independent walking in patients with large-core acute ischemic stroke. However, it was also associated with an increased risk of developing intracranial bleeding. Non-contrast head CT with CTA scans may be appropriate for screening patients to undergo ET.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.051 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".