Efficacy and safety of endovascular treatment for patients with acute intracranial atherosclerosis–related posterior circulation stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
The benefit of endovascular treatment (EVT) for patients with intracranial atherosclerosis-related large vessel occlusion (ICAS-LVO) in posterior circulation stroke (PCS) is inconsistent. This systematic review and meta-analysis were conducted to estimate the effect of ICAS-LVO in PCS treated by EVT. A systematic review was completed, tracking studies from their date of inception until February 2020. Clinical studies which compared outcomes after EVT for ICAS-LVO and non-ICAS-LVO in PCS were included. Data were synthesized and interpreted from meta-analysis. A total of 688 patients (352 ICAS-LVO and 336 non-ICAS-LVO) in the eight studies were included. The successful reperfusion rate (odds ratio [OR], 0.58; 95% confidence intervals [95% CIs], 0.37-0.93; P = 0.02) was lower in PCS with ICAS-LVO than non-ICAS-LVO. And for other clinical outcomes, there were no differences between both groups. Moreover, there were no statistical differences of any clinical outcome among subgroups stratified by nations and target vessel occlusion location. With respect to patients' characteristics, age (mean difference [MD], -2.75; 95% CI, -4.62--0.88; P = 0.004), pc-Alberta Stroke Program Early CT Score (MD, -0.49; 95% CI, -0.94--0.05; P = 0.03), distributions of sex (male) (OR, 2.34; 95% CI, 1.53-3.56; P < 0.001), prior or current smoking (OR, 1.85; 95% CI, 1.12-3.07; P = 0.02), hypertension (OR, 2.06; 95% CI, 1.32-3.22; P = 0.002), coronary artery disease (OR, 0.27; 95% CI, 0.11-0.66; P = 0.004) and general anesthesia (OR, 2.89; 95% CI, 1.54-5.45; P = 0.001) were statistically different between both groups. In conclusion, more targeted assessments are warranted for patients with ICAS-LVO-related PCS during clinical strategies, and the benefit of EVT for PCS with ICAS-LVO deserves further research.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".