B.6 Endovascular treatment of acute ischemic stroke in patients with pre-morbid disability: a meta-analysis
Bibliographic record
Abstract
Background: Trials of endovascular thrombectomy (EVT) for acute stroke have excluded patients with pre-morbid disability. We performed a meta-analysis to assess the effectiveness and safety of EVT in patients with pre-morbid disability. Methods: According to PRISMA guidelines, we searched for studies describing outcomes in patients with pre-morbid disability (modified Rankin Scale [mRS] 2-5), treated with EVT or medical management (MM). Random-effects meta-analysis was used to pool outcomes including return to baseline mRS at 90 days, symptomatic ICH (sICH), and 90-day mortality. Results: We analyzed 14 studies of patients with pre-morbid disability (mRS2-5: EVT=1,373, MM=253). Compared to medical therapy, EVT was associated with higher likelihood of return to baseline mRS (OR=2.37, 95%CI:1.39-4.04) and a trend towards lower mortality (OR=0.68, 95%CI:0.46-1.02), with similar odds of sICH (OR 1.01, 95%CI:0.49-2.08). In studies comparing patients with vs. without pre-morbid disability treated with EVT, similar results were found except that pre-morbid disability, when defined more strictly as mRS 3-5, was associated with mortality (OR 3.49, p<0.001). Conclusions: In patients with pre-morbid disability, EVT carries a higher chance of return to baseline mRS compared to patients treated with MM or without pre-morbid disability, although with higher mortality than patients without pre-morbid disability. These findings merit validation with randomized controlled trials.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.073 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".