Endovascular Thrombectomy for Low ASPECTS Large Vessel Occlusion Ischemic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The current American Heart Association guidelines for acute ischemic stroke reserve Grade 1A recommendation for the use of endovascular thrombectomy (EVT) for patients with an Alberta Stroke Program Early Computed Tomography Score (ASPECTS) of ≥6. OBJECTIVE: We aim to determine the safety and efficacy of EVT for large vessel occlusion ischemic stroke patients with low ASPECTS (5 or less). METHODS: Medline, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched for studies appraising the outcomes of EVT for low ASPECTS ischemic stroke. A meta-analysis of proportions compared the clinical outcomes of patients undergoing EVT and those receiving best medical therapy only. RESULTS: Nine studies (1,196 patients) were included. There was a trend (p = 0.11) toward a higher rate of symptomatic intracranial hemorrhage (sICH) in the EVT group (9.2%; 95% CI 6.1-13.6; I2 53.37%) compared to the medical group (5.5%; 95% CI 3.7-8.1; I2 0%). There was no difference (p = 0.41) in the pooled 90-day mortality of EVT patients (30.7%; 95% CI 21.7-41.5; I2 84.23%) and medical patients (36.6%; 95% CI 26.4-48.1; I2 76.2%). EVT patients had better (p = 0.001) 90-day outcomes, with 27.7% (95% CI 21.8-34.5; I2 62.08%) of patients attaining a modified Rankin Scale of 0-2 compared to only 3.7% (95% CI 2.3-5.9; I2 87.21%) in the medical group. CONCLUSIONS: This meta-analysis demonstrates a trend in higher sICH among low ASPECTS patients undergoing EVT. Despite this, a significant proportion of this subset of patients still achieved good functional outcomes at 90 days. Randomized trials are necessary to substantiate this result as significant bias is inherent in the observational studies included in this review.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".