Risk Factors of Futile Recanalization Following Endovascular Treatment in Patients With Large‐Vessel Occlusion: Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Although successful recanalization (modified Thrombolysis in Cerebral Infarction score 2b–3) can be achieved in >80% of patients experiencing stroke attributable to large‐vessel occlusion, up to 50% of patients may develop poor clinical outcomes (modified Rankin scale score at 90 days of 3–6), termed as futile recanalization (FR). The meta‐analysis aims to determine various risk factors associated with FR. Methods In February 2021, a comprehensive literature search on risk factors associated with FR was performed with keywords, including “stroke,” “thrombectomy,” “treatment outcome,” and “risk factors.” Their correlations with FR were evaluated using the random effect size meta‐analysis model. Results Twenty studies with 3037 patients were included with an FR rate of 51.0%. Our meta‐analyses showed that age (mean difference [MD], 5.6; 95% CI, 4.7–6.6), National Institutes of Health Stroke Scale score (MD, 4.2; 95% CI, 3.2–5.1), Alberta Stroke Program Early Computed Tomography (CT) Score (MD, −0.5; 95% CI, 0.8–0.3), hypertension (odds ratio [OR], 1.5; 95% CI, 1.3–1.9), systolic blood pressure (MD, 6.9; 95% CI, 3.6–8.7), atrial fibrillation (OR, 1.5; 95% CI, 1.2–1.8), IV tPA (tissue‐type plasminogen activator) (OR, 0.7; 95% CI, 0.5–0.8), puncture to recanalization time (MD, 9.6; 95% CI, 5.3–13.8), and time of onset to recanalization (MD, 32.1; 95% CI, 6.5–47.7) were significantly associated with FR ( P <0.001). Conclusions Age, admission National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT Score, comorbidities, including hypertension, systolic blood pressure, atrial fibrillation, and use of IV tPA, as well as time frames, including onset to recanalization and onset to arrival, were significant influencing factors for FR after mechanical thrombectomy. Future research on the mechanism underlying FR is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".