Mechanical Thrombectomy in Acute Ischemic Stroke: A Systematic Review
Bibliographic record
Abstract
Although intravenous thrombolysis increases the probability of a good functional outcome in carefully selected patients with acute ischemic stroke, a substantial proportion of patients who receive thrombolysis do not have a good outcome. Several recent trials of mechanical thrombectomy appear to indicate that this treatment may be superior to thrombolysis. We therefore conducted a systematic review and meta-analysis to evaluate the clinical effectiveness and safety of new-generation mechanical thrombectomy devices with intravenous thrombolysis (if eligible) compared with intravenous thrombolysis (if eligible) in patients with acute ischemic stroke caused by a proximal intracranial occlusion. We systematically searched seven databases for randomized controlled trials published between January 2005 and March 2015 comparing stent retrievers or thromboaspiration devices with best medical therapy (with or without intravenous thrombolysis) in adults with acute ischemic stroke. We assessed risk of bias and overall quality of the included trials. We combined the data using a fixed or random effects meta-analysis, where appropriate. We identified 1579 studies; of these, we evaluated 122 full-text papers and included five randomized control trials (n=1287). Compared with patients treated medically, patients who received mechanical thrombectomy were more likely to be functionally independent as measured by a modified Rankin score of 0-2 (odds ratio, 2.39; 95% confidence interval, 1.88-3.04; I2=0%). This finding was robust to subgroup analysis. Mortality and symptomatic intracerebral hemorrhage were not significantly different between the two groups. Mechanical thrombectomy significantly improves functional independence in appropriately selected patients with acute ischemic stroke.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".