First-Pass Effect in Basilar Artery Occlusions: Insights From the Endovascular Treatment of Ischemic Stroke Registry
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the settings of thrombectomy, the first-pass effect (FPE), defined by a complete recanalization after one pass with no rescue therapy, has been shown to be associated with an improved outcome. As this phenomenon has been predominantly described in anterior circulation strokes, we aimed to study the prevalence, outcomes, and predictors of FPE in patients with a basilar artery occlusion. METHODS: From a prospective multicentric registry, we collected the data of all consecutive basilar artery occlusion patients who underwent thrombectomy and compared the outcomes of patients who achieved FPE and those who did not. We also compared FPE patients with those who achieved a complete recanalization with >1 pass. Finally, a multivariate analysis was performed to determine the predictors of FPE. RESULTS: Data from 280 patients were analyzed in our study, including 84 of 280 patients (30%) with an atheromatous etiology. An FPE was achieved in 93 patients (33.2%), with a significantly higher proportion of good outcomes (modified Rankin Scale score 0-2 at 3 months) and lower mortality than non-FPE patients. An FPE was also associated with improved outcomes compared with patients who went on to have full recanalization with >1 pass. Contact aspiration as first-line strategy was a strong predictor of FPE, whereas baseline antiplatelets and atheromatous etiology were negative predictors. CONCLUSIONS: In our study, an FPE was achieved in approximately one-third of patients with a basilar artery occlusion and was associated with improved outcomes. More research is needed to improve devices and techniques to increase the incidence of FPE. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03776877.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".