Abstract TP12: The Trend of Successful First Pass in M2 Segment Stroke Thrombectomy- Insights From the STAR Collaboration
Bibliographic record
Abstract
Introduction: Stroke thrombectomy devices and the experience of neurointerventionists have improved significantly over the last few years making targeting distal occlusions such as of the M2 segment of the middle cerebral artery more feasible. We aimed to study the trend in the successful first pass (SFP) of M2 occlusions over time using the data from a contemporary multicenter registry. Methods: We reviewed the data from the Stroke Thrombectomy and Aneurysm Registry (STAR), which included data from 11 thrombectomy-capable stroke centers to identify stroke patients who underwent mechanical thrombectomy of M2 segment occlusion. SFP was defined by achieving modified Thrombolysis in Cerebral Infarction (mTICI) score≥2b with a single thrombectomy device pass. We analyzed the linear trendline of the rate of SFP over time. Then, we used a logistic regression model to assess predictors of SFP of M2 segment occlusion. Results: We included 401 patients who underwent stroke thrombectomy of M2 occlusion; median age was 71 (IQR 60-80), 212 (52.9%) were females, 174 (43.4%) were white, National Institute of Health stroke scale (NIHSS) was 14 (IQR 8-19), Alberta Stroke Program Early CT (ASPECT) score on presentation was 9 (IQR 7-10) and onset wot groin time was 287 (IQR 181-454). SFP was achieved in 118 (29.4%) patients (linear trendline over time is in Figure 1). Presenting after 2014 was an independent predictor of SFP (OR 1.9, 95% CI 1.1-3.2, P=0.019) after controlling for age, sex, NIHSS on presentation, intravenous alteplase (IV-tPA), and onset to groin time. Conclusion: SFP rate of M2 segment occlusion has increased after 2014 likely secondary the improvement in stroke thrombectomy devices and neurointerventionists experience.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".