Predictors of good outcomes and mortality after thrombectomy for basilar artery occlusion within 12 hours of onset
Bibliographic record
Abstract
BACKGROUND: Patients with acute basilar artery occlusion (ABAO) who undergo combined standard medical treatment (SMT) and endovascular thrombectomy (EVT) may still have unsatisfactory outcomes. This study was conducted to identify the factors that may impact their outcomes. METHODS: We retrospectively reviewed the data of all patients with ABAO combined with SMT and EVT in the endovascular treatment for acute basilar artery occlusion (ATTENTION) trial. A good outcome is defined as a modified Rankin Scale (mRS) score of 0-3, a poor outcome as mRS score of 4-6, and mortality as death at 90-day follow-up. The study analyzed various factors influencing the patients' good outcomes and mortality. RESULTS: The study included 221 patients (148 men and 73 women). Among these patients, 45.7% achieved an mRS score of 0-3, while the overall mortality rate was 37.1% (82/221). A good outcome was significantly associated with younger age (adjusted OR 0.96; 95% CI 0.93 to 0.99; P=0.019), a baseline posterior circulation Alberta Stroke Program Early CT Score (pc-ASPECTS) of 8-10 (adjusted OR 2.34; 95% CI 1.07 to 5.12; P=0.034), and post-procedure pc-ASPECTS of 8-10 (adjusted OR 1.40; 95% CI 1.07 to 1.84; P=0.013). Additionally, time from puncture to reperfusion (adjusted OR 2.02; 95% CI 1.2 to 3.41; P=0.008) and intracranial hemorrhage (adjusted OR 3.59; 95% CI 1.09 to 11.8; P=0.035) were associated with 90-day mortality. CONCLUSIONS: Younger age, baseline pc-ASPECTS of 8-10, and higher post-procedure pc-ASPECTS could effectively predict good outcomes for patients with ABAO undergoing EVT. Additionally, a prolonged time from puncture to reperfusion and intracranial hemorrhage can independently predict mortality. TRIAL REGISTRATION NUMBER: NCT04751708.
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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 | 0.005 |
| 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".