First pass effect as an independent predictor of functional outcomes in medium vessel occlusions: An analysis of an international multicenter study
Bibliographic record
Abstract
INTRODUCTION: First pass effect (FPE), achievement of complete recanalization (mTICI 2c/3) with a single pass, is a significant predictor of favorable outcomes for endovascular treatment (EVT) in large vessel occlusion stroke (LVO). However, data concerning the impact on functional outcomes and predictors of FPE in medium vessel occlusions (MeVO) are scarce. PATIENTS AND METHODS: We conducted an international retrospective study on MeVO cases. Multivariable logistic modeling was used to establish independent predictors of FPE. Clinical and safety outcomes were compared between the two study groups (FPE vs non-FPE) using logistic regression models. Good outcome was defined as modified Rankin Scale 0-2 at 3 months. RESULTS: Eight hundred thirty-six patients with a final mTICI ⩾ 2b were included in this analysis. FPE was observed in 302 patients (36.1%). In multivariable analysis, hypertension (aOR 1.55, 95% CI 1.10-2.20) and lower baseline NIHSS score (aOR 0.95, 95% CI 0.93-0.97) were independently associated with an FPE. Good outcomes were more common in the FPE versus non-FPE group (72.8% vs 52.8%), and FPE was independently associated with favorable outcome (aOR 2.20, 95% CI 1.59-3.05). 90-day mortality and intracranial hemorrhage (ICH) were significantly lower in the FPE group, 0.43 (95% CI, 0.25-0.72) and 0.55 (95% CI, 0.39-0.77), respectively. CONCLUSION: Over 2/3 of patients with MeVOs and FPE in our cohort had a favorable outcome at 90 days. FPE is independently associated with favorable outcomes, it may reduce the risk of any intracranial hemorrhage, and 3-month mortality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".