Current status of postoperative care for elderly osteoporotic fracture patients in Jiangsu Province, China: A multicenter cross-sectional study.

BACKGROUND: The postoperative care behavior of elderly osteoporotic fracture patients has a significant impact on their prognosis and rehabilitation; thus, it is critical to completely comprehend their current state of care before optimizing postoperative care strategies. AIMS: To determine the current status of postoperative care for elderly osteoporotic fracture patients in Jiangsu Province. METHODS: From October to December 2023, 669 elderly postoperative patients with osteoporotic fractures were recruited for a cross-sectional study via convenience sampling and a self-designed questionnaire from 21 hospitals in seven urban areas in Jiangsu Province. RESULTS: A total of 800 questionnaires were distributed, and 709 questionnaires were recovered, for a return rate of 88.6%. The postoperative support and care received by the patients were mostly by their children and spouses, and the postoperative health education knowledge received by the patients was mostly about fracture-related knowledge and precautions for the use of medications. Health education methods were mainly conducted by medical staff explanations and health brochures; only 45.3% of the patients were treated with anti-osteoporosis therapy. CONCLUSIONS: The current status of postoperative care for osteoporotic fracture patients in Jiangsu Province varies somewhat according to geographic location, hospital level and other factors, with tertiary hospitals and more economically developed areas having relatively better care outcomes. For in-hospital care, it is necessary to improve the content and methods of in-hospital education. For out-of-hospital care, it is necessary to raise the level of awareness of anti-osteoporosis treatment and the prevention and treatment of re-fractures, and to improve hospital-community referral services.

The effectiveness of home rehabilitation based on BPMpathway exercise rehabilitation system for patients after total knee arthroplasty in China.

BACKGROUND: There is significant interest in the use of web-based technologies for rehabilitation of patients after total knee arthroplasty (TKA). BPMpathway is a combination of a wireless BPMpro sensor and mobile app to provide a personalized post-operative support programme for TKA patients. OBJECTIVE: To investigate the impact of the BPMpathway exercise rehabilitation system on home rehabilitation for TKA patients. METHODS: This study had a quasi-experimental design and was conducted with 42 inpatients after TKA. Patients were divided into a control group (n = 21) and an intervention group (n = 21) according to sequence of hospitalization. After discharge, patients in the control group received routine follow-up outside the hospital, while the intervention group, underwent out-of-hospital transitional care utilizing the BPMpathway exercise rehabilitation system. Knee function scores (HSS), quality of life scores (SF-36), and functional exercise compliance scores were compared between the two groups at 1, 3, and 6 months after TKA. RESULTS: The between-group differences in knee function were statistically significant at 1, 3, and 6 months post-operation (t = 6.299, 10.021, 13.064, all P < 0.05). There were between-group significant differences observed in functional exercise compliance and quality of life at 1 and 3 months (t = 7.166, 2.435, both P < 0.05; t = 2.879, 3.117, both P < 0.05). However, there were no significant differences at 6 months in these two respects (t = 0.167, 0.901, both P > 0.05). CONCLUSIONS: The BPMpathway exercise rehabilitation system seems to be potential effective in improving knee function, early compliance with functional exercise and quality of life in TKA patients.

Analysis of Bone Loss around Cemented and Biologic Prostheses after Hemiarthroplasty.

Objective: To perform hemiarthroplasty (HA) on elderly patients with femoral neck fractures using cemented and biologic prostheses and then compare the bone loss around the two types of prostheses after surgery. Methods: A total of 60 patients aged over 75 years (with a mean age of 83.5 years) and suffering from femoral neck fracture (Garden types III and IV) from January 2018 to December 2020 were selected; they were randomly divided into group A (n = 30, cemented prostheses) and group B (n = 30, biologic prostheses) and received HA. At 1 month, 6 months, and 12 months after surgery, Harris Hip Scale (HHS) was adopted for patient evaluation, and patients' bone mineral density (BMD) of the 7 Gruen zones around the prostheses was measured by dual-energy X-ray absorptiometry (DEXA scan). Results: Both groups achieved satisfactory results postoperatively, and the Harris scores of the hips increased gradually over time, which were better in group A than in group B. Gruen zones in group A showed a slower trend of decreasing BMD than group B, and the differences were significant at zones 2, 3, and 4 (P < 0.05). Conclusion: For elderly patients with femoral neck fractures, selecting cemented prosthesis for HA better recovers the hip function and has a low rate of bone loss around the prosthesis.

Subspace Clustering via Structured Sparse Relation Representation.

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of "relation reconstruction." We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.

Improved linear classifier model with Nyström.

Most data sets consist of interlaced-distributed samples from multiple classes and since these samples always cannot be classified correctly by a linear hyperplane, so we name them nonlinearly separable data sets and corresponding classifiers are named nonlinear classifiers. Traditional nonlinear classifiers adopt kernel functions to generate kernel matrices and then get optimal classifier parameters with the solution of these matrices. But computing and storing kernel matrices brings high computational and space complexities. Since INMKMHKS adopts Nyström approximation technique and NysCK changes nonlinearly separable data to linearly ones so as to reduce the complexities, we combines ideas of them to develop an improved NysCK (INysCK). Moreover, we extend INysCK into multi-view applications and propose multi-view INysCK (MINysCK). Related experiments validate the effectiveness of them in terms of accuracy, convergence, Rademacher complexity, etc.