The majority of fracture research has been conducted using long bone fracture models, with significantly less research into the mechanisms driving craniofacial repair. However, craniofacial bones differ from long bones in both their developmental mechanism and embryonic origin. Thus, it is possible that their healing mechanisms could differ. In this study we utilize stabilized and unstabilized mandible fracture models to investigate the pathways regulating repair. Whereas fully stable trephine defects in the ramus form bone directly, mechanical motion within a transverse fracture across the same anatomical location promoted robust cartilage formation before boney remodeling. Literature investigating long bone fractures show chondrocytes are a direct precursor of osteoblasts during endochondral repair. Lineage tracing with Aggrecan‐CreERT2::Ai9 tdTomato mice demonstrated that mandibular callus chondrocytes also directly contribute to the formation of new bone. Furthermore, immunohistochemistry revealed that chondrocytes located at the chondro‐osseous junction expressed Sox2, suggesting that plasticity of these chondrocytes may facilitate this chondrocyte‐to‐osteoblast transformation. Based on the direct role chondrocytes play in bone repair, we tested the efficacy of cartilage grafts in healing critical‐sized mandibular defects. Whereas empty defects remained unbridged and filled with fibrous tissue, cartilage engraftment produced bony‐bridging and robust marrow cavity formation, indicating healthy vascularization of the newly formed bone. Engrafted cartilage directly contributed to new bone formation since a significant portion of the newly formed bone was graft/donor‐derived. Taken together these data demonstrate the important role of chondrocyte‐to‐osteoblast transformation during mandibular endochondral repair and the therapeutic promise of using cartilage as a tissue graft to heal craniofacial defects.

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Recent advances show that Neural Architectural Search (NAS) method is able to find state-of-the-art image classification deep architectures. In this paper, we consider the oneshot NAS problem for resource constrained applications. This problem is of great interest because it is critical to choose different architectures according to task complexity when the resource is constrained. Previous techniques are either too slow for one-shot learning or does not take the resource constraint into consideration. In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy. Specifically, we propose to formulate the RC-DARTS task as a constrained optimization problem by adding the resource constraint. An iterative projection method is proposed to solve the given constrained optimization problem. We also propose a multi-level search strategy to enable layers at different depths to adaptively learn different types of neural architectures. Through extensive experiments on the Cifar10 and ImageNet datasets, we show that the RC-DARTS method learns lightweight neural architectures which have smaller model size and lower computational complexity while achieving comparable or better performances than the state-of-the-art methods.

Objectives: To analyze a series of claims from a large national malpractice insurer associated with fracture care to understand what parameters are associated with claims, defense costs, and paid indemnity. Design: Review of claims in fracture care settings from a national database; case series. Setting: Database draws from insured pool of 400,000 medical malpractice cases from 400 healthcare entities across the country, representing 165,000 physicians; both academic and private. Patients/Participants: Fracture care patients bringing legal suit. Main Outcome Measurements: Cost of legal proceedings and indemnity, ICD-9 codes, and contributing causes toward claims. Results: A total of 756 fracture claims were asserted between 2005 and 2014 regarding fracture care within the database; 70% were brought for inaccurate, missed, or delayed diagnosis, while 22% addressed medical treatment and 8% were for surgical management. Orthopaedics was the primary service in 22%. Total cost (expenses and indemnity) to orthopaedic providers totaled $13.1MM (million). The most common claim against orthopaedics was for fractures of the tibia and fibula (11.4%). Impact factor (IF) analysis (as described by Matsen) of indemnity in these cases reveals 3 fracture regions of highest indemnity burden: fractures of the tibia and fibula (IF: 1.86, 11.4%), pelvis (IF: 1.77, 6.6%), and spine (IF 1.33, 6.6%). Analysis of contributing factors identifies the category of clinical judgement as the most common category (62%). Other common factors include patient noncompliance (31%), communication (28%), technical skill (17%), clinical systems (11%), and documentation (10%). The single most common specific cause of a claim in orthopaedic fracture care was misinterpretation of diagnostic imaging (25%). Conclusion: This study is the first of its kind to identify fractures of the tibia and fibula as high risk for litigation against orthopaedic providers and provides general counseling of legal pitfalls in fracture care. Finally, we are able to identify the act of patient assessment as a key issue in over half of all fracture-related claims against orthopaedic providers. Providers in general and specialty settings can use this information to help guide their treatment and care ownership decisions in the care of patients with fractures. Level of Evidence: Economic - Level III.

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