Showing 661 - 680 results of 18,177 for search 'significantly ((((teer decrease) OR (greater decrease))) OR (((we decrease) OR (a decrease))))', query time: 0.52s Refine Results
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    Presentation 1_Prehospital tranexamic acid decreases early mortality in trauma patients: a systematic review and meta-analysis.pdf by Yi Li (1144)

    Published 2025
    “…</p>Conclusion<p>Prehospital TXA decreases early (24-h) mortality in trauma patients without a significant increase in the risk of VTE and other complications, and further studies are still needed to improve and optimize its management strategy.…”
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    Raw data. by Jia Zhu (135506)

    Published 2025
    “…The remaining working conditions did not exhibit a significant difference. However, the observed decreasing trend was consistent with previously documented research. …”
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    Fig 4 - by Hyuk Sung Yoon (20208672)

    Published 2024
    Subjects:
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    Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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    Hyperparameter ranges by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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    Convolutional vs RNN context encoder by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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