Trends in revascularization therapies for patients with acute stroke with large infarcts: A population-based study
Bibliographic record
Abstract
Background Evidence from randomized clinical trials shows that mechanical thrombectomy (MT) enhances functional outcomes in patients with large core ischemic stroke. Objective To evaluate trends in the use of revascularization therapies, particularly MT, and their impact on functional outcomes in patients with large core ischemic stroke in routine clinical settings. Methods Observational data from the Stroke Code Registry of Catalonia (CICAT, 2016–2024) were analyzed. Patients with anterior circulation ischemic stroke and Alberta Stroke Program Early CT Score (ASPECTS) <6, whether treated with reperfusion therapies or not, were included. Statistical analyses included trend analysis and multivariable logistic regression to identify predictors of favorable outcomes (modified Rankin Scale score 0–3 at 90 days) and mortality. Results Among 599 patients, MT use increased significantly from 22% pre-2022 to 36% post-2022. This increase was associated with improved functional outcomes, with favorable outcomes rising from 29% to 43% post-2022. MT was a significant independent predictor of favorable outcomes (OR 3.4, 95% CI 2.1 to 5.5) and reduced mortality (OR 0.46, 95% CI 0.32 to 0.68). Intravenous thrombolysis also improved outcomes (OR 2.1, 95% CI 1.3 to 3.5). The benefit of MT was consistent across ASPECTS subgroups (0–2 and 3–5). Mediation analysis indicated that 88% of improvement could be attributed to increased MT use. Conclusions Increased MT use significantly improved outcomes for patients with large core ischemic stroke, particularly after 2022. Benefits were observed across subgroups, including those with very low ASPECTS. These findings support broadening MT access and suggest the need to update treatment guidelines to consider patients with large ischemic cores, aiming to optimize outcomes in routine clinical practice.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".