Mechanical thrombectomy in low Alberta Stroke Program Early Computed Tomographic Score: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background Due to their poor natural history and lack in level-I evidence, patients with large vessel occlusion and large core infarcts (Alberta Stroke Program Early Computed Tomographic Score <6) have been excluded from receiving mechanical thrombectomy. This systematic review and meta-analysis seeks to summarize current evidence from published randomized controlled trials to compare the safety and efficacy of mechanical thrombectomy with optimal medical therapy in treating stroke patients with large core infarcts. Methods We searched PubMed and EMBASE for randomized controlled trials investigating the safety and efficacy of mechanical thrombectomy vs optimal medical therapy in patients presenting with large vessel occlusion and large infarcts. Basic demographic and comorbidities were assessed, and clinical outcomes were compared, including modified Rankin scale 0–3, and 0–2 at 3 months, symptomatic intracranial hemorrhage, decompressive hemicortectomy, and 90-day mortality. Results Three randomized controlled trials totaling 1011 patients (501 and 510 in the medical management and mechanical thrombectomy arm, respectively) were included. Patients undergoing mechanical thrombectomy had significantly higher odds of achieving better functional outcomes at 3 months: Modified Rankin scale 0–2 (OR = 3.05, 95% CI = 2.101–4.4021, p < 0.0001) and modified Rankin scale 0–3 (OR = 2.20, 95% CI = 1.67–2.89, p < 0.0001) as compared to those receiving optimal medical management. There were no differences between groups in 90-day mortality (OR = 0.93, 95% CI = 0.70–1.23, p = 0.60), symptomatic intracranial hemorrhage (OR = 1.89, 95% = CI 0.95–3.77, p = 0.07) or decompressive hemicraniectomy (OR = 1.25, 95% CI = 0.69–2.25, p = 0.46). Conclusion Mechanical thrombectomy for patients with large infarcts is associated with improved functional outcomes and a similar safety profile compared to optimal medical management. Ongoing trials will help better refine the target population that benefits the most from treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.089 | 0.035 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".