Reperfusion-dependent treatment effects of thrombectomy in patients with large ischemic infarcts
Bibliographic record
Abstract
BACKGROUND: While thrombectomy benefits patients with large infarcts, it is unclear whether this benefit persists across different levels of reperfusion. AIMS: This study investigates how the degree of reperfusion influences the effectiveness of endovascular thrombectomy (EVT) combined with best medical treatment (BMT), compared to BMT alone, in patients with large infarcts. METHODS: This post hoc analysis of the TENSION trial, a randomized controlled study, assessed EVT versus BMT in patients with extensive infarction (Alberta Stroke Program Early CT Score (ASPECTS) 3-5). Primary outcome was the modified Rankin Scale (mRS) score at 90 days. Secondary outcomes included infarct volume at 24 h, mortality, and symptomatic hemorrhage. Outcomes were stratified by final reperfusion level, measured with the modified thrombolysis in cerebral infarction (mTICI) scale. Confounder-adjusted common odds ratios (cORs) and average treatment effects (ATEs) were estimated using inverse probability weighting with regression adjustment. RESULTS: A total of 246 patients (median age, 74 years (interquartile range (IQR), 65-80); median baseline ASPECTS, 4 (IQR, 3-5)) were included. Compared to BMT alone, unsuccessful EVT (mTICI ⩽ 2a) was not associated with worse functional outcomes (cOR:1.2, 95% CI, 0.95 to 1.52; p = 0.131), higher mortality (ATE: -11.6%; 95% CI, -28.82 to 5.61; p = 0.187), or larger infarct volumes on follow-up (ATE:0.99 mL; 95% CI, -45.30 to 45.32; p = 0.965). First-pass complete reperfusion (mTICI 3) showed the greatest treatment benefit, significantly improving all endpoints, with a cOR of 4.85 (95% CI, 3.74-6.31; p < 0.001) for improved mRS scores and a 29% absolute reduction in mortality. CONCLUSION: In this post hoc analysis of the TENSION trial, unsuccessful EVT did not worsen outcomes compared to BMT alone. The highest benefit of EVT occurred with first-pass complete reperfusion, emphasizing the importance of achieving optimal reperfusion in this vulnerable stroke subgroup. These findings do not justify general treatment recommendations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".