Benefits of First Pass Recanalization by Initial Infarct Burden for Basilar Artery Strokes
Bibliographic record
Abstract
ABSTRACT Background and aims: Achieving a first pass recanalization (FPR) improves clinical outcomes in patients with basilar artery strokes, but its association with initial infarct burden is unknown. We aimed to study the benefits of FPR for basilar artery strokes by initial infarct burden using the Posterior Circulation Alberta Stroke Program Early CT score (pc-ASPECTS). Methods: We retrospectively analyzed the prospective multicentric Endovascular Treatment of Ischemic Stroke registry and included 194 patients diagnosed with an acute basilar artery occlusion who were treated with thrombectomy. Our primary outcome was a modified Rankin Scale (mRS) of 0–3 at 90 days, and our secondary outcomes were an mRS of 4–6 and mortality. We compared the 90-day clinical outcomes of achieving an FPR versus multiple thrombectomy passes based on patients’ initial infarct size on pretreatment MRI: small (pc-ASPECTS = 9–10), medium (pc-ASPECTS = 6–8) and large (pc-ASPECTS <6). Results: Patients with a medium or large infarct size had significantly better outcomes (mRS 0–3 at 3 months) if FPR was achieved than if multiple passes were required (RR = 1.61, 95% CI: 1.16, 2.24; p-value = 0.005; and RR = 3.41, 95% CI: 1.54–7.57; p-value = 0.003, respectively). No similar difference was seen among patients with small infarcts. Achieving an FPR was also associated with a significantly lower mortality risk among patients with a moderate infarct size (RR = 0.36, 95% CI: 0.17–0.79; p-value = 0.010) but not with those with small or large infarcts. Conclusions: Achieving an FPR significantly improves clinical outcomes in acute stroke patients with basilar artery occlusions undergoing thrombectomy when their infarcts are medium or large. Ongoing research to develop surgical techniques to achieve FPR is crucial to improving patients’ prognoses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".