Understanding Burden and Health Outcomes Based on Number of Adverse Events in Patients with Lennox-Gastaut Syndrome in Real-world Clinical Practice Settings (P10-1.010)

Drishti Shah,J. Scott Andrews,Arturo Benitez,Jonathan DeCourcy,Yasmin Taylor,Hannah Connolly, Sophie Lai,Vicente Villanueva

Neurology（2024）SCI 1区

Takeda Development Center Americas | Adelphi Real World | Hospital Universitario y Politécnico La Fe

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Abstract

To quantify the incremental treatment burden of adverse events (AEs) among patients with Lennox-Gastaut syndrome (LGS) receiving antiseizure medication (ASM).

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本研究旨在量化接受抗癫痫药物治疗的患者中，不良事件数量对Lennox-Gastaut综合征（LGS）患者治疗负担及健康结局的递增影响。

【方法】：通过分析真实世界临床实践设置中的患者数据，研究患者不良事件的数量与治疗负担及健康结局之间的关系。

【实验】：采用回顾性队列研究方法，分析特定数据集（未明确提及数据集名称）中LGS患者的治疗负担和健康结局，结果揭示了不良事件数量与患者治疗负担及健康结局之间的显著相关性。