Infarct Growth despite Successful Endovascular Reperfusion in Acute Ischemic Stroke: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Infarct volume inversely correlates with good recovery in stroke. The magnitude and predictors of infarct growth despite successful reperfusion via endovascular treatment are not known. PURPOSE: We aimed to summarize the extent of infarct growth in patients with acute stroke who achieved successful reperfusion (TICI 2b-3) after endovascular treatment. DATA SOURCES: We performed a systematic review and meta-analysis by searching MEDLINE and Google Scholar for articles published up to October 31, 2020. STUDY SELECTION: Studies of >10 patients reporting baseline and post-endovascular treatment infarct volumes on MR imaging were included. Only patients with TICI 2b-3 were included. We calculated infarct growth at a study level as the difference between baseline and follow-up MR imaging infarct volumes. DATA ANALYSIS: Our search yielded 345 studies, and we included 10 studies reporting on 973 patients having undergone endovascular treatment who achieved successful reperfusion. DATA SYNTHESIS: The mean baseline infarct volume was 19.5 mL, while the mean final infarct volume was 37.5 mL. A TICI 2b reperfusion grade was achieved in 24% of patients, and TICI 2c or 3 in 76%. The pooled mean infarct growth was 14.8 mL (95% CI, 7.9-21.7 mL). Meta-regression showed higher infarct growth in studies that reported higher baseline infarct volumes, higher rates of incomplete reperfusion (modified TICI 2b), and longer onset-to-reperfusion times. LIMITATIONS: Significant heterogeneity among studies was noted and might be driven by the difference in infarct growth between early- and late-treatment studies. CONCLUSIONS: These results suggest considerable infarct growth despite successful endovascular treatment reperfusion and call for a faster workflow and the need for specific therapies to limit infarct growth.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".