Significance of Baseline Ischemic Core Volume on Stroke Outcome After Endovascular Therapy in Patients Age ≥75 Years: A Pooled Analysis of Individual Patient Data From 7 Trials
Bibliographic record
Abstract
BACKGROUND: Age and infarct volume are strong predictors of outcome in patients with ischemic stroke who underwent endovascular therapy (EVT). We aimed to investigate the impact of ischemic core volume (ICV) on stroke outcome after EVT in elderly. METHODS: Using the HERMES (Highly Effective Reperfusion Using Multiple Endovascular Devices) collaboration, a patient-level meta-analysis of 7 randomized trials in which patients were enrolled from December 2010 to April 2015) dataset, we categorized patients into those aged <75 and ≥75 years. ICV was calculated on computed tomography perfusion or magnetic resonance diffusion-weighted imaging. The association between ICV and the benefit of EVT over best medical treatment on outcome (modified Rankin Scale [mRS] at 90 days) and an ICV threshold for high likelihood (≥90%) of very poor outcome (mRS score ≥5) after EVT were investigated. RESULTS: <0.001). In patients aged ≥75 years, modeling of outcome in both treatment arms revealed potential loss of effect for EVT at ICV of ≥50 mL or ≥85 mL for achieving mRS score ≤3 or ≤4, respectively. Treatment effect of EVT was significant in ICV <50 mL for mRS ≤3 (odds ratio 2.38, 95% confidence interval 1.35-4.22). ICV ≥132 mL was a threshold for high likelihood of very poor outcome after EVT. However, EVT still predicted at least 30% rate of mRS ≤3 at 150 mL ICV if near-complete or complete reperfusion was achieved. CONCLUSIONS: Baseline ICV has an impact on stroke outcome after EVT in the elderly, but elderly patients with large ICV may still benefit from EVT if near-complete or complete reperfusion is achieved. Young patients seem to benefit from EVT regardless of ICV status.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".