Firstline a direct aspiration first pass technique versus firstline stent retriever for acute basilar artery occlusion: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Acute basilar artery occlusion (BAO) can result in extremely high disability and mortality. Stent retrievers (SRs) can achieve a high recanalization rate for BAO, therefore improving favorable outcomes. However, the efficacy of a direct aspiration first pass technique (ADAPT) to treat BAO is unclear. Our aim was to compare the efficacy and safety of firstline ADAPT with that of firstline SR for patients with acute BAO. METHODS: Three databases were systematically searched for literature reporting outcomes on thrombectomy for acute BAO with both firstline ADAPT and firstline SR. The modified Newcastle-Ottawa scale was applied to assess bias risk. The random effects model was used. RESULTS: Of 50 articles, 5 cohort studies (2 prospective and 3 retrospective) were included in our research. 193 cases were treated with firstline ADAPT and 283 cases received firstline SR. Successful recanalization rate was significantly higher in the firstline ADAPT group (OR=2.0, 95% CI 1.1 to 3.5). Procedure time (mean difference=-27.6 min, 95% CI -51.0 to -4.3) and the incidence of new territory embolic event (OR=0.2, 95% CI 0.05 to 0.83) was significantly less in the firstline ADAPT group. No significant difference was observed between the firstline ADAPT and firstline SR groups for rate of complete recanalization, rescue therapy, any hemorrhagic complication, favorable outcomes, or mortality at 90 days. CONCLUSIONS: Our meta-analysis suggested that for patients with acute BAO, firstline ADAPT might achieve higher and faster recanalization, comparable neurological improvement and safety compared with firstline SR. Further studies are needed to confirm these results.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".