Mechanical Thrombectomy in Basilar Artery Occlusion
Bibliographic record
Abstract
BackgroundMechanical thrombectomy (MT) of basilar artery occlusions (BAO) is a subject of debate. We investigated the clinical outcome of MT in BAO and predictors of a favorable outcome.Material and MethodsA total of 104 MTs of BAO (carried out between 2010 and 2016) were analyzed. Favorable outcome as a modified Rankin scale (mRS) ≤ 2 at 90 days was the primary endpoint. The influence of the following variables on outcome was investigated: number of detectable posterior communicating arteries (PcoAs), patency of basilar tip, completeness of BAO and posterior circulation Alberta Stroke Program early computed tomography score (PC-ASPECTS). Secondary endpoints were technical periprocedural parameters including symptomatic intracranial hemorrhage (sICH).ResultsThe favorable clinical outcome at 90 days was 25% and mortality was 43%. The rate of successful reperfusion, i.e. modified thrombolysis in cerebral infarction (mTICI) ≥ 2b was 82%. Presence of bilateral PcoAs (area under the curve, AUC: 0.81, odds ratio, OR: 4.2, 2.2–8.2; p < 0.0001), lower National Institute of Health Stroke Scale (NIHSS) on admission (AUC: 0.74, OR: 2.6, 1.3–5.2; p < 0.01), PC-ASPECTS ≥ 9 (AUC: 0.72, OR: 4.2, 1.5–11.9; p < 0.01), incomplete BAO (AUC: 0.66, OR: 2.6, 1.4–4.8; p < 0.001), and basilar tip patency (AUC: 0.66, OR: 2.5, 1.3–4.8; p < 0.01) were associated with a favorable outcome. Stepwise logistic regression analysis revealed that the strongest predictors of favorable outcome at 90 days were bilateral PcoAs, low NIHSS on admission, and incomplete BAO (AUC: 0.923, OR: 7.2, 3–17.3; p < 0.0001).ConclusionThe use of MT for BAO is safe with high rates of successful reperfusion. Aside from baseline NIHSS and incomplete vessel occlusion, both known predictors of favorable outcome in anterior circulation events, we found that collateral flow based on the presence or absence of PcoAs had a decisive prognostic impact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".