Predictors of futile recanalization after endovascular treatment in acute ischemic stroke: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite successful recanalization after endovascular treatment, many patients with acute ischemic stroke due to large vessel occlusion still show functional dependence, namely futile recanalization. METHODS: PubMed and Embase were searched up to April 30, 2021. Studies that reported risk factors for futile recanalization following endovascular treatment of acute ischemic stroke were included. The mean difference (MD) or odds ratio (OR) and 95% confidence interval (95% CI) of each study were pooled for a meta-analysis. RESULTS: Twelve studies enrolling 2138 patients were included. The pooled analysis showed that age (MD 5.81, 95% CI 4.16 to 7.46), female sex (OR 1.40, 95% CI 1.16 to 1.68), National Institutes of Health Stroke Scale (NIHSS) score (MD 4.22, 95% CI 3.38 to 5.07), Alberta Stroke Program Early CT Score (ASPECTS) (MD -0.71, 95% CI -1.23 to -0.19), hypertension (OR 1.73, 95% CI 1.43 to 2.09), diabetes (OR 1.78, 95% CI 1.41 to 2.24), atrial fibrillation (OR 1.24, 95% CI 1.01 to 1.51), admission systolic blood pressure (MD 4.98, 95% CI 1.87 to 8.09), serum glucose (MD 0.59, 95% CI 0.37 to 0.81), internal carotid artery occlusion (OR 1.85, 95% CI 1.17 to 2.95), pre-treatment intravenous thrombolysis (OR 0.67, 95% CI 0.55 to 0.83), onset-to-puncture time (MD 16.92, 95% CI 6.52 to 27.31), puncture-to-recanalization time (MD 12.37, 95% CI 7.96 to 16.79), and post-treatment symptomatic intracerebral hemorrhage (OR 6.09, 95% CI 3.18 to 11.68) were significantly associated with futile recanalization. CONCLUSION: This study identified female sex, comorbidities, admission systolic blood pressure, serum glucose, occlusion site, non-bridging therapy, and post-procedural complication as predictors of futile recanalization, and also confirmed previously reported factors. Further large-scale prospective studies are needed.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".