Endovascular treatment for distal basilar artery occlusion stroke
Bibliographic record
Abstract
Background This study aimed to investigate the clinical outcomes of endovascular treatment (EVT) for distal basilar artery occlusion (BAO) and compare them with the outcomes of standard medical treatment (SMT) in daily clinical practice. Methods Patients with distal BAO enrolled in the BASILAR study from January 2014 to May 2019 were included. Differences in clinical outcomes were analyzed using Pearson's chi-square test and multivariable logistic regression. Clinical outcomes were evaluated using the modified Rankin Scale (mRS) score at 90 days, the mortality at 90 days, and the occurrence of symptomatic intracranial hemorrhage within 48 h. Results Among the 267 patients with distal BAO (222 patients in the EVT group and 45 patients in the SMT group), compared with the SMT group, the EVT group was associated with a favorable outcome (mRS 0–3; 40.1 vs. 15.6%; aOR 5.44; 95% CI, 1.68–17.66; P = 0.005) and decreased mortality (44.6 vs. 71.1%, aOR 0.32, 95% CI, 0.13–0.77; P = 0.012). In the EVT group, multivariable analysis showed that the initial National Institutes of Health Stroke Scale (NIHSS) score and posterior circulation-Alberta Stroke Program Early CT Score (pc-ASPECTS) were associated with favorable functional outcomes and mortality. Conclusion Our study suggests that, compared with SMT, EVT is technically feasible and safe for patients with distal BAO.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".