Abstract TP23: Mortality Risk in Acute Ischemic Stroke Patients With Large Vessel Occlusion Treated With Mechanical Thrombectomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Recent randomized-controlled clinical trials (RCTs) have provided solid evidence that mechanical thrombectomy (MT) coupled with best medical therapy (BMT) improve functional outcomes of acute ischemic stroke (AIS) patients with large vessel occlusion (LVO) compared to BMT alone. However, they provided inconclusive evidence on the benefit of MT on mortality. Methods: We evaluated the association of MT+BMT compared to BMT with the risk of three-month mortality using aggregate data from all available RCTs. We also sought to identify potential predictors on the mortality risk and performed univariate meta-regression analyses. Results: Our literature search identified 11 eligible RCTs, including a total of 2,460 patients. The pooled rates of 3-month mortality were 15% (95%CI:12-19%) and 19% (95%CI:16-23%), respectively, in the MT+BMT and BMT groups. In the overall analysis MT+BMT was associated with a significantly lower risk for 3-month mortality compared to BMT (Risk Ratio=0.83, 95% confidence interval:0.69-0.99; p=0.04), without heterogeneity across included studies (I 2 =3%,p for Cochran Q=0.41). No evidence of publication bias was present in funnel plot inspection and Egger’s statistical test (p=0.762). In meta-regression analyses no moderating effect on the aforementioned association was detected with patient age (p=0.254), gender (p=0.702), admission systolic blood pressure (p=0.601), admission glucose (p=0.277), onset-to-groin puncture time (p=0.985), administration of intravenous alteplase prior to MT (p=0.804), MT under general anesthesia (p=0.735) and successful reperfusion following MT (p=0.663). Conclusion: Our meta-analysis provides evidence that MT+BMT reduce the risk of three-month mortality compared to BMT alone. This association appears not to be moderated by individual patient or procedural characteristics.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".