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Abstract The exploration of traumatic spinal fractures (TSFs) within the senior demographic has not been thoroughly scrutinized, particularly with respect to variations across genders, age groups, seasonal periods, and causative factors. This retrospective analysis aimed to dissect differences in the prevalence and characteristics of TSFs among the elderly, factoring in gender, age, seasonal timing, and causation. A retrospective analysis was conducted on the medical and imaging records of 1,415 patients, all aged 60 years or older, who were diagnosed with TSFs from 2013 to 2019. This study categorized the data by gender, age groups (60–70, 70–80, and 80 years or older), seasons, and the cause of injuries, including road traffic crashes (RTCs), falls from low heights (LHF), falls from high heights (HHF), and injuries incurred during everyday activities and agricultural labor (DFI). Male patients exhibited notably higher incidences of RTCs, high-height falls (HHFs), outdoor incidents, comas post-injury, fractures of the lower limbs (LLFs), pelvic fractures (PFs), rib fractures (RFs), intra-thoracic injuries (ITIs), intra-abdominal injuries (IAIs), cervical fractures, and spinal cord injuries (SCIs). With advancing age, there was a marked decline in occurrences of RTCs, HHFs, outdoor incidents, RFs, craniocerebral injuries (CCIs), ITIs, cervical fractures, and SCIs, while the incidences of DFIs, indoor incidents, and thoracic and lumbar (T + L) fractures notably increased. During autumn, LLF occurrences were significantly reduced, whereas the winter season saw an increase in thoracic fractures. Spring time was associated with a higher frequency of lumbar fractures and noncontiguous spinal fractures (NSFs). Significant distinctions were observed in the age distribution, injury circumstances, associated injuries, and SCIs between high-energy impacts (RTCs and HHFs) and low-energy traumas (LHFs and DFIs). In the elderly demographic, TSFs exhibited discernible distinctions based on gender, age, seasonal variations, and etiological factors, impacting the nature and circumstances of injuries, associated traumas, complications, fracture sites, and the occurrence of SCIs.

Abstract The efficacy of Mesh optimized versus standard percutaneous vertebroplasty (PVP) for osteoporotic vertebral compression fractures. Grid optimization (102 cases; 38 men, 64 women aged 67.3 ± 8.5) and traditional PVP groups (94 cases) were identified from 196 PVP patients treated from May 2016 to 2019. The optimal puncture site and angle forced bone cement into both groups before surgery. The main indexes were operation time, X-ray fluoroscopy times, bone cement injection volume, leakage, VAS, and injured vertebrae height. Preoperative general data were equivalent between groups (P > 0.05). All patients survived surgery without spinal cord injury, incision infection, pulmonary embolism, or death. The mesh optimization group had improved operation time (34.8 ± 6.5 min), fluoroscopy times (29.5 ± 5.5), bone cement injection volume (5.3 ± 2.1 ml), and bone cement permeability greater (3.9 percent; 4/98) than the standard PVP group (P 0.05). However, in the mesh optimization group, measurements improved to 1.8 ± 0.4 mm and (1.8 ± 0.3) mm by month three and to 1.7 ± 0.3 mm at last follow-up (P

Abstract To investigate the sex, age, role and geographic differences in traumatic spinal fractures (TSFs) caused by motor vehicle collisions (MVCs) in adults (≥ 18 years old). This was a multicentre retrospective observational study. In total, 798 patients with TSFs caused by MVCs admitted to our hospitals from January 2013 to December 2019 were enrolled. The patterns were summarized with respect to different sexes (male and female), age group (18–60 and ≥ 60), role (driver, passenger and pedestrian) and geographic location (Chongqing and Shenyang). Significant differences in distribution related to district (p = 0.018), role (p

Abstract In order to augment the efficacy of the intelligent evaluation model for assessing the suitability of ice and snow tourism, this study refines the model by incorporating the Long Short-Term Memory (LSTM) network within the framework of the Internet of Things (IoT). The investigation commences with an elucidation of the application of IoT technology in environmental detection. After this, an analysis is conducted on the structure of LSTM and its merits in the realm of time series prediction. Ultimately, a novel model for appraising the suitability of ice and snow tourism is formulated. The efficacy of this model is substantiated through empirical experiments. The results of these experiments reveal that the refined model exhibits exceptional performance across diverse climatic conditions, encompassing mild, cold, humid, and arid climates. In regions characterized by mild climates, the predictive accuracy of the refined model progressively ascends from 88% in the initial quarter to 94% in the fourth quarter, surpassing the capabilities of conventional models. Consistently robust performance is demonstrated by the refined model throughout each quarter. In terms of operational efficiency, comparative analysis indicates that the refined model attains a moderate level, manifesting a 30–33 s runtime and maintaining a Central Processing Unit (CPU) usage rate between 40 and 43%. This observation implies that the refined model adeptly balances precision against resource consumption. Consequently, this study holds significance as a scholarly reference for the integration of IoT and LSTM networks in the domain of tourism evaluation.