ArticleFrontiers in neurology
Predicting intravenous thrombolysis outcomes in acute ischemic stroke using machine learning.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Objective: Intravenous thrombolysis remains a cornerstone intervention for improving clinical outcomes in patients with acute ischemic stroke (AIS). Accurate prediction of poor functional outcomes following thrombolysis is essential for optimizing individualized treatment and guiding clinical decision-making. This study aimed to develop machine learning-based models for early post-treatment reassessment of post-thrombolysis outcomes in AIS patients, thereby providing a reliable tool for early prognostic reassessment after thrombolysis. Methods: A total of 383 AIS patients who received intravenous thrombolysis between November 2024 and November 2025 were retrospectively enrolled and randomly assigned to a training set ( Results: Baseline characteristics were well-balanced between groups (all Conclusion: The proposed prediction model demonstrates satisfactory performance in estimating functional outcomes following thrombolysis in AIS patients. The identified core variables may offer valuable insights for clinical prognosis and the design of individualized thrombolytic strategies.
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