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Titlebook: Hip Arthroscopy and Hip Joint Preservation Surgery; Shane J. Nho,Michael Leunig,Bryan T. Kelly Reference work 20151st edition Springer Sci

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樓主: Grant
51#
發(fā)表于 2025-3-30 10:06:22 | 只看該作者
Clinical Biomechanics of the Hip Jointbeen shown to influence the movement and forces at the hip joint. Understanding the normal osteokinematics, arthrokinematics, and muscle actions at the hip joint provides clinicians with the basic biomechanical background to detect impairments that may impact function and contribute to injury. Human
52#
發(fā)表于 2025-3-30 13:58:38 | 只看該作者
53#
發(fā)表于 2025-3-30 18:35:54 | 只看該作者
54#
發(fā)表于 2025-3-30 21:22:16 | 只看該作者
Layered Concept of the Hip and Pelvisbe reviewed with attention paid to structure and function. The most common pathologic conditions affecting the young patient with hip pain will also be reviewed, with an emphasis on the interaction between the layers.
55#
發(fā)表于 2025-3-31 02:21:22 | 只看該作者
56#
發(fā)表于 2025-3-31 08:39:35 | 只看該作者
Operative Indications for Hip Arthroscopy and Open Hip Preservation Surgeryts strengths and limitations are better defined through increased study and use, have also evolved. These include the presence of advance stages of osteoarthritis, inflammatory arthritis, various forms of hip dysplasia, chronic muscle pathology, preexisting neurologic injury, and greater trochanter
57#
發(fā)表于 2025-3-31 11:29:53 | 只看該作者
58#
發(fā)表于 2025-3-31 16:44:04 | 只看該作者
TOM) and finding cancer specific gene clusters using Spectral Clustering (SC). This is followed by a filtering step to extract a much-reduced set of crucial genes using best first search with support vector machine (BFS-SVM). Finally, artificial neural networks, SVM, and K-nearest neighbor classifie
59#
發(fā)表于 2025-3-31 20:46:22 | 只看該作者
Laura E. Thorpdern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery..The capability of learning representations from structures directly without using any predefined structu
60#
發(fā)表于 2025-4-1 00:37:07 | 只看該作者
Philip J. Malloy,Shane J. Nhoombines the low-bias relevance estimator with state-of-the-art relevance estimators in order to enhance their accuracy. The experimental validation on 20 publicly available cancer expression datasets shows the robustness of a selection approach which is not biased by a specific learner.
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