Bridging therapy versus direct endovascular thrombectomy in patients with established large infarct: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Whether patients with large core infarctions should undergo intravenous thrombolysis (IVT) before endovascular thrombectomy (EVT) is currently a subject of controversy. The study aimed to investigate the association of prior use of IVT with outcomes of EVT patients with large core infarctions. MATERIALS AND METHODS: This prospective cohort included patients with acute large vessel occlusion and Alberta Stroke Program Early Computed Tomography Score (ASPECTS) of 0-5 from 38 stroke centers across China between November 2021 and February 2023. The primary outcome was defined as favorable functional outcomes, which is 90-day modified Rankin Scale (mRS) scores ranging from 0 to 3. Procedural outcomes included measures of successful and effective recanalization. Safety outcomes included the incidence of any intracranial hemorrhage (ICH), symptomatic ICH, and 90-day mortality. RESULTS: Of 490 patients, 122 (24.5%) were treated with IVT before EVT. Bridging therapy and its transfer modes showed no association with any of the measured outcomes. Compared to direct EVT, bridging therapy was associated with a decreased risk of symptomatic ICH in very elderly patients and a decreased risk of any ICH in patients with admission NIHSS scores of 20 or higher. Additionally, early stroke severity may alter the odds of any ICH in patients with bridging therapy versus direct EVT (inverse probability weighting adjusted P value for interaction=0.003 and 0.007, respectively). CONCLUSION: In large core infarction patients with high admission NIHSS or very elderly age, bridging therapy appears to have some advantages over direct EVT in reducing the risk of ICH.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".