Titlebook: Limb Lengthening and Reconstruction Surgery Case Atlas; Trauma • Foot and An S. Robert Rozbruch,Reggie c. Hamdy Reference work 2015 Springe

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Case 7: A 12 cm Traumatic Femoral Defect Treated with a Long Oblique Diaphyseal Femoral Osteotomy an 1 mm/day. The monorail was turned on average four or more full turns in the later stages to achieve 1 mm/day of femoral lengthening. Weekly radiographs helped chart the progress. The lengthening required 4 months to complete. As soon as the appropriate femoral length was restored, the retrograde ro

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Case 8: Femoral Bone Defect anesthesia. At 8-month follow-up, the nonunion site had consolidated and the wounds had healed with no evidence of drainage. His knee flexion improved from 90° to 130°. He was able to dorsiflex his foot to neutral with the knee in extension and to just past neutral with the knee flexed. He also rep

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Case 10: Infected Nonunion of the Tibiamately underwent Ilizarov frame fixation, resulting in successful healing of the fracture. Her treatment also included IV antibiotics, antibiotic cement, and autologous bone graft. The enhanced stability of our construct allowed full weight bearing, aided pain control, and facilitated the patient’s

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Reference work 2015atured, including external fixation, internal fixation, combination approaches and fully implantable limb lengthening nails. This case-based approach is an efficient and thorough way to learn this exciting new frontier in orthopedic surgery. .

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Processes

arameters. We evaluate SimuMan objectively and subjectively to demonstrate its fidelity, naturalness, and real-time. The experimental results show that the SimuMan system is characterized by low latency, good interactivity, easy operation, good robustness, and wide application.

affinity

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ization of the physical entities. The model specifies the behavior of digital twin including geometric physical modeling, data-driven model and mechanism model. The data illustrates the data in cyberspace sensing from physical entites. The context represents the application context for digital twins

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David Fraser,Kevin Tetsworth filtering, and .th-order additive Markov chain. First, UBSI collects users’ check-in sequence and interaction behavior data, generates interest weight using a weight model, integrates personalized data using collaborative filtering method, and finally applies the weighted .th-order additive Markov

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