Predictors of Futile Recanalization in Patients With Basilar Artery Occlusion With Large Versus Nonlarge Infarcts
Bibliographic record
Abstract
BACKGROUND: Basilar artery occlusion is associated with high rates of disability and mortality, and despite advances in endovascular treatment, futile recanalization remains a challenge. This study aims to identify predictors of futile recanalization in patients with basilar artery occlusion, focusing on large and nonlarge infarcts. METHODS AND RESULTS: This multicenter retrospective study included patients from 65 centers across China. Patients were categorized based on posterior circulation Alberta Stroke Program Early CT [Computed Tomography] Score (pc-ASPECTS) into 2 groups: large infarcts (pc-ASPECTS ≤6) and nonlarge infarcts (pc-ASPECTS >6). Predictors of futile recanalization-defined as a modified Rankin Scale score of 4 to 6 at 90 days despite successful recanalization-were analyzed using logistic regression models. Among the 2075 patients, 1113 (53.6%) experienced futile recanalization. In patients with pc-ASPECTS >6, predictors of futile recanalization included older age (odds ratio [OR], 1.18 [95% CI, 1.06-1.31]), higher National Institute of Health Stroke Scale scores (OR, 1.75 [95% CI, 1.58-1.94]), and prolonged time from puncture to reperfusion (OR, 1.24 [95% CI, [1.12-1.38]). Intravenous thrombolysis (OR, 0.85 [95% CI, [0.77-0.94]) and achieving modified Thrombolysis in Cerebral Infarction grade 3 (OR, 0.81 [95% CI, [0.74-0.90]) were associated with a lower likelihood of futile recanalization. In patients with pc-ASPECTS ≤6, being male (OR, 0.75 [95% CI, 0.58-0.96]) and having higher pc-ASPECTS scores (OR, 0.65 [95% CI, 0.48-0.85]) were protective against futile recanalization, whereas higher National Institute of Health Stroke Scale scores increased the risk (OR, 1.81 [95% CI, 1.42-2.32]). CONCLUSIONS: This study identifies distinct predictors of futile recanalization in patients with basilar artery occlusion based on infarct size. The findings underscore the importance of individualized treatment strategies and timely intervention to optimize endovascular treatment outcomes in high-risk patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".