Objective: We focus on providing the first comprehensive national dataset on the incidence, injury aetiology and mortality of TSCI in China.
Methods: A multi-stage stratified cluster sampling method was used. We included TSCI cases from all hospitals in three regions, nine provinces and 27 cities in China via search of electronic medical records and retrospectively analysed the characteristics of TSCI in China from 2009 to 2018. We estimated the incidence of TSCI in the total population and subgroups.
Results: There were 5954 actual cases in 2009, corresponding to a total estimated TSCI incidence of 45.1 cases per million population (95% CI, 44.0-46.3). There were 10,074 actual cases in 2018, corresponding to a total estimated TSCI incidence of 66.5 cases per million population (95% CI, 65.2-67.8) (P < 0.001; annual average percentage change (AAPC), 4.4%). From 2009 to 2018, the incidence of almost all sex/age groups showed an increasing trend over time (P < 0.001; AAPC, 0.7-8.8%). The elderly population (aged 65-74) displayed the highest incidence of TSCI (with an average annual incidence of 127.1 cases per million [95% CI, 119.8-134.3]).
Conclusions: The TSCI incidence increased significantly from 2009 to 2018. The incidence in the elderly populations was consistently high and continues to increase over time. The mortality of TSCI patients in hospitals is relatively low and continues to decrease each year, but elderly individuals remain at a high risk of hospital death.
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http://dx.doi.org/10.1007/s00586-021-06957-3 | DOI Listing |
World J Urol
January 2025
Research & Analysis Services, University Hospital Basel, Steinengraben 36, Basel, 4051, Switzerland.
Background: Multidisciplinary teams (MDTs) are essential for cancer care but are resource-intensive. Decision-making processes within MDTs, while critical, contribute to increased healthcare costs due to the need for specialist time and coordination. The recent emergence of large language models (LLMs) offers the potential to improve the efficiency and accuracy of clinical decision-making processes, potentially reducing costs associated with traditional MDT models.
View Article and Find Full Text PDFAm J Cancer Res
December 2024
Department of Reproductive Medicine, The First Affiliated Hospital, Jinan University Guangzhou 510000, Guangdong, China.
This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in endometrial carcinoma (EC) patients, thus improving the identification of patients at high risk of LNM and further providing accurate support for clinical decision-making. This retrospective analysis included 541 cases of EC treated at The First Affiliated Hospital, Jinan University between January 2017 and January 2022. Various clinical and pathological variables were incorporated, including age, body mass index (BMI), pathological grading, myometrial invasion, lymphovascular space invasion (LVSI), estrogen receptor (ER) and progesterone receptor (PR) levels, and tumor size.
View Article and Find Full Text PDFCase Rep Dent
January 2025
Department of Dentistry, NEIGRIHMS (North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences), Shillong, India.
The prevalence of oromaxillofacial fracture in pediatric patients is comparatively less than in adults, which could be due to several inconclusive factors, such as infrequent exposure to high-contact sports games, rash driving of vehicles and motorbikes, alcohol consumption, and fist fights for personal reasons under the influence of alcohol. More importantly, most of the time, children are under the care of their parents till they reach an age of maturity. One more thing that everyone believes even today is the elasticity nature of their bones as well as their body weight during their growing stage.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
Sci Rep
January 2025
College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, 010010, China.
In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program.
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