Abstract WP12: Endovascular Thrombectomy for Acute Ischemic Stroke Associated With Cervical Artery Dissection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: Strokes associated with cervical artery dissection have been managed primarily with antithrombotics with poor outcomes. The additive role of endovascular thrombectomy remains unclear. The objective was to perform systematic review and meta-analysis to compare endovascular thrombectomy and medical therapy for acute ischemic stroke associated with cervical artery dissection. Methods: Studies from six electronic databases included outcomes of patient cohorts with acute ischemic stroke secondary to cervical artery dissection who underwent treatment with endovascular thrombectomy. A meta-analysis of proportions was conducted with a random-effects model. Modified Rankin score at 90 days (mRS 0-2) was the primary outcome. Other outcomes included proportion of patients with thrombolysis in cerebral infarction (TICI) 2b-3 flow, 90-day mortality rate, and 90-day symptomatic intracerebral hemorrhage (sICH) rate. Results: Six studies were included, comprising 193 cases that underwent thrombectomy compared with 59 cases that were managed medically. Successful recanalization with a pooled proportion of thrombolysis in cerebral infarction (TICI) 2b-3 flow in the thrombectomy group was 74%. Favorable outcome (mRS 0-2) was superior in the pooled thrombectomy group (62.9%, 95% CI 55.8-69.5%) compared medical management (41.5%, 95% CI 29.0-55.1%, P=0.006). The pooled rate of 90-day mortality was similar for endovascular vs medical (8.6% vs 6.3%). The pooled rate of symptomatic intracranial haemorrhage (sICH) did not significantly differ (5.9% vs 4.2%, P=0.60). Conclusions: Current data suggest that endovascular thrombectomy may be an option in patients with acute ischemic stroke due to cervical artery dissection. This requires further confirmation in higher quality prospective studies.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".