Endovascular Treatment for Acute Basilar Artery Occlusion: A Fragility Index Meta-Analysis
Bibliographic record
Abstract
Introduction: High-quality evidence regarding the use of endovascular treatment (EVT) in patients with acute basilar artery occlusion (BAO) has been provided by recently completed randomized controlled clinical trials (RCTs). Methods: We conducted a systematic review and meta-analysis including all available RCTs that investigated efficacy and safety of EVT in addition to best medical treatment (BMT) versus BMT alone for BAO. The random-effects model was used, while the fragility index (FI) was calculated for dichotomous outcomes of interest. Results: Four RCTs were included comprising a total of 988 patients with acute BAO (mean age: 65.6 years, 70% men, median NIHSS: 24, 39% pretreatment with intravenous thrombolysis). EVT was related to higher likelihood of good functional outcome (RR: 1.54; 95% CI: 1.16–2.05; I2 = 60%), functional independence (RR: 1.83; 95% CI: 1.08–3.08; I2 = 79%) and reduced disability at 3 months (adjusted common OR: 1.96; 95% CI: 1.26–3.05; I2 = 59%) compared to BMT alone. Despite that EVT was associated with a higher risk for symptomatic intracranial hemorrhage (RR: 7.78; 95% CI: 2.36–25.61; I2 = 0%) and any intracranial hemorrhage (RR: 2.85; 95% CI: 1.50–5.44; I2 = 16%), mortality at 3 months was lower among patients that received EVT plus BMT versus BMT alone (RR: 0.76; 95% CI: 0.65–0.89; I2 = 0%). However, sufficient robustness was not evident in any of the reported associations (FI < 10) including the overall effect regarding the primary outcome. The former associations were predominantly driven by RCTs with recruitment limited in China. Conclusions: EVT combined with BMT is associated with a higher likelihood of achieving good functional outcomes and a lower risk of death at 3 months compared to BMT alone, despite the higher risk of sICH. An individual-patient data meta-analysis is warranted to uncover and adjust for potential sources of heterogeneity and to provide further insight.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".