Abstract TP153: Basilar Artery Occlusion Management: Specialist Perspectives From After The BEST Of BASICS Study
Bibliographic record
Abstract
Background: Two early basilar artery occlusion (BAO) randomized controlled trials did not establish superiority of endovascular thrombectomy (EVT) over medical management. While many providers continue to recommend EVT for acute BAO, perceptions of equipoise in randomizing patients with BAO to medical management may differ between clinician specialties. Methods: We conducted an international survey (1/1/22-3/31/22) regarding management strategies in acute BAO prior to the announcement of 2 trials indicating superiority of EVT, and compared responses between interventionalists (INT) and non-interventionalists (nINT). Selection practices for routine EVT based on neuroimaging and clinical features were compared between the two groups using descriptive statistics. Results: Among the 1245 respondents (nINT=702), INT more commonly believed that EVT was superior to medical management in acute BAO (98.5% vs. 95.1%, p<0.01). A similar proportion of INT and nINT responded that they would not randomize a patient with BAO to EVT (29.4% vs. 26.7%), or that they would only under specific circumstances (p=0.45). Among respondents who would recommend EVT, there was no difference in the maximum pre-stroke disability, minimum stroke severity, or infarct burden on computed tomography between the two groups (p>0.05), although nINT more commonly preferred perfusion imaging (24.2% vs. 19.7%, p=0.04). Among respondents who indicated they would randomize to medical management, INT were more likely to randomize when the NIHSS was ≥10 (15.9% vs. 6.9%, p<0.01). Conclusions: Following the publication of two neutral clinical trials in BAO EVT, most stroke providers believed EVT to be superior to medical management in carefully selected patients, with most indicating they would not randomize a patient to medical treatment. There were small differences in preference of advanced neuroimaging, although these preferences were unsupported by clinical trial data.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".