Abstract P488: The Impact of First Pass Effect on Acute Ischemic Stroke Clinical Outcomes: Why We Need to Continue to Improve Mechanical Thrombectomy?
Bibliographic record
Abstract
Background: Mechanical thrombectomy (MT) has significantly improved outcomes of acute ischemic stroke (AIS) patients due to large vessel occlusion (LVO). The first-pass effect (FPE), defined as achieving complete reperfusion (mTICI3/2c) with a single pass, was reported to be associated with higher functional independence rates following EVT and has been emphasized as an important procedural target. We compared MT outcomes in patients who achieved FPE to those who did not in a real world large database. Method: A retrospective analysis of LVO pts who underwent MT from a single center prospectively collected database. Patients were stratified into those who achieved FPE and non-FPE. The primary outcome (discharge and 90 day mRS 0-2) and safety (sICH, mortality and neuro-worsening) were compared between the two groups. Results: Of 580 pts, 261 (45%) achieved FPE and 319 (55%) were non-FPE. Mean age was (70 vs 71, p=0.051) and mean initial NIHSS (16 vs 17, p=0.23) and IV tPA rates (37% bs 36%, p=0.9) were similar between the two groups. Other baseline characteristics were similar. Non-FPE pts required more stenting (15% vs 25%, p=0.003), and angioplasty (19% vs 29%, p=0.01). The FPE group had significantly more instances of discharge (33% vs 17%, p<0.001), and 90-day mRS score 0-2 (29% vs 20%, p<0.001), respectively. Additionally, the FPE group had a significant lower mean discharge NIHSS score (12 vs 17, p<0.001). FPE group had better safety outcomes with lower mortality (14.2% vs 21.6%, p=0.03), sICH (5.7% vs 13.5, p=0.004), and neurological worsening (71.3% vs 78.4%, p=0.02), compared to the non-FPE group. Conclusion: Patients with first pass complete or near complete reperfusion with MT had higher functional independence rates, reduced mortality, symptomatic hemorrhage and neurological worsening. Improvement in MT devices and techniques is vital to increase first pass effect and improve clinical outcomes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".