Abstract TP197: Impact of First-Pass Effect on Clinical Outcomes in Stroke Patients With Low Aspects
Bibliographic record
Abstract
Introduction: The first-pass effect (FPE), defined as a complete or near-complete recanalization after a single pass of a mechanical thrombectomy (MT) device, has been linked to favorable clinical outcomes. However, its effectiveness in acute ischemic stroke (AIS) patients with low ASPECTS (Alberta Stroke Program Early CT Score 2-5) has not been validated. Method: We utilized data from STAR, a multicenter database of 84 centers worldwide, to conduct a retrospective, cohort study on patients who underwent MT for internal carotid artery (ICA) or M1 occlusion presenting with ASPECTS 2-5. We compared the outcomes of patients who achieved FPE (successful recanalization with an mTICI score of 2c or higher in one pass) to those who did not. The primary outcome was a 90-day favorable outcome, defined as mRS 0-3. Secondary outcomes included any intracranial hemorrhage (ICH), symptomatic ICH (sICH), and 90-day mortality. Results: Out of 10,229 patients in the STAR database, 250 patients met our inclusion criteria. Among those, 60 (24%) achieved FPE. There were no significant differences between the two groups in baseline, imaging, and procedural characteristics. FPE was significantly associated with higher odds of 90-day mRS 0-3 (adjusted odds ratio (aOR): 2.17, 95% confidence interval (CI): 1.04 - 4.20; P-value: 0.04) and lower rates of ICH (OR: 0.49, 95% CI: 0.25 - 0.93, P-value: 0.03). However, there was no significant difference in sICH rates (OR: 0.84, 95% CI: 0.31 - 2.04, P-value: 0.70). Conclusion: Achieving FPE in AIS stroke patients with low ASPECTS was associated with significantly higher rates of good functional outcomes and lower rates of ICH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".