From therapeutic nihilism to armamentarium: A meta-analysis of randomized clinical trials assessing safety and efficacy of endovascular therapy for acute large ischemic strokes
Bibliographic record
Abstract
BackgroundThree recent randomized clinical trials (RCTs) investigated the potential benefit of endovascular therapy (EVT) in acute ischemic stroke patients presenting with large infarcts. We aimed to confirm the safety and efficacy of EVT in patients presenting with large infarcts and provide more precise estimations of the treatment effects using study-level meta-analysis.MethodsComprehensive search of MEDLINE database through PubMed till February 2023 was performed including RCTs only. The data were then extracted from the selected studies and pooled as risk ratio (RR) with 95% confidence interval (95% CI).ResultsThere were a total of 1005 patients across the three qualifying RCTs. Regarding the functional outcomes assessed by modified Rankin Scale (mRS) score, the analyzed data demonstrated statistically significant differences in favor of thrombectomy for both independent ambulatory status (mRS 0–3: RR = 1.78, 95% CI [1.28, 2.48], p = 0.0006) and functional independence (mRS 0–2: RR = 2.54, 95% CI [1.85, 3.48], p < 0.001). The analyzed data did not demonstrate any statistically significant differences between EVT and medical management alone in terms of 90-day mortality (RR = 0.95, 95% CI [0.78, 1.16], p = 0.61), symptomatic intracranial hemorrhage (RR = 1.83, 95% CI [0.95, 3.55], p = 0.07), and need for hemicraniectomy (RR = 1.22, 95% CI [0.43, 3.41], p = 0.71).ConclusionThis study confirms the benefit of EVT on functional outcomes of patients presenting with large ischemic infarcts without significant differences in the rates of symptomatic intracranial hemorrhage, hemicraniectomy, or 90-day mortality.
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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 | 0.052 | 0.070 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.075 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".