Abstract WP3: Predictors and Outcomes of Successful First Pass in Neurothrombectomy- Insights From the STAR Collaboration
Bibliographic record
Abstract
Introduction: Shorter procedure time during neurothrombectomy is a strong predictor for good outcomes in stroke patients with large vessel occlusion. We sought to assess the predictors and outcomes of successful first pass (SFP) using multi-center investigator-initiated database. Methods: Prospectively collected neurothrombectomy data from 11 thrombectomy-capable stroke centers was combined in the Stroke Thrombectomy and Aneurysm Registry (STAR). SFP was defined by achieving modified Thrombolysis in Cerebral Infarction (mTICI) score≥2b with a single thrombectomy device pass. We compared the baseline characteristics, procedural metrics, rate of symptomatic intracranial hemorrhage (sICH), and long-term functional outcomes between SFP and non-SFP patients. A multivariate logistic regression analysis was used to assess the predictors of SFP and evaluate whether SFP was an independent predictor for good long-term functional outcomes (90-day mRS≤2). Results: A total of 733 SFP patients and 1134 non-SFP patients were included in this analysis. SFP patients were older (73 vs. 70, P=0.001), had higher Alberta Stroke Program Early CT (ASPECT) score on presentation (9 vs. 8, P=0.002). The use of Solumbra technique was an independent predictor of SFP (OR 1.2, 95% CI 1.1-1.4, P=0.004) after controlling for age, sex, location of occlusion, National Institute of Health stroke scale (NIHSS) on presentation, intravenous alteplase (IV-tPA), and onset to groin (OTG) time. SFP was an independent predictor for good long-term functional outcomes (OR1.6, 95% CI 1.1-2.3, P=0.008) after controlling for age, sex, location of occlusion, NIHSS on presentation, OTG time, IV-tPA, procedure technique, and procedure duration. Conclusion: SFP lead to higher rates of functional independence in stroke patients with large vessel occlusion. These records reiterate the importance of SFP as a benchmark measure for stroke thrombectomy devices.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".