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11.
Alexander Real Chierika Ukogu Divya Krishnamoorthy Nicole Zubizarreta Samuel K. Cho Andrew C. Hecht James C. Iatridis 《The spine journal》2019,19(2):225-231
Background Context
Low back pain (LBP) is a common complaint in clinical practice of multifactorial origin. Although obesity has been thought to contribute to LBP primarily by altering the distribution of mechanical loads on the spine, the additional contribution of obesity-related conditions such as diabetes mellitus (DM) to LBP has not been thoroughly examined.Purpose
To determine if there is a relationship between DM and LBP that is independent of body mass index (BMI) in a large cohort of adult survey participants.Study Design
Retrospective analysis of prospectively collected National Health and Nutrition Examination Survey (NHANES) data to characterize associations between LBP, DM, and BMI in adults subdivided into 6 subpopulations: normal weight (BMI 18.5–25), overweight (BMI 25–30), and obese (BMI >30) diabetics and nondiabetics. Diabetes was defined with glycohemoglobin A1c (HbA1c) ≥6.5%.Patient Sample
11,756 participants from NHANES cohort.Outcome Measures
Percentage of LBP reported.Methods
LBP reported in the 1999-2004 miscellaneous pain NHANES questionnaire was the dependent variable examined. Covariates included HbA1c, BMI, age, and family income ratio to poverty as continuous variables as well as race, gender, and smoking as binary variables. Individuals were further subdivided by weight class and diabetes status. Regression and graphical analyses were performed on the study population as a whole and also on subpopulations.Results
Increasing HbA1c did not increase the odds of reporting LBP in the full cohort. However, multivariate logistic regression of the 6 subpopulations revealed that the odds of LBP significantly increased with increasing HbA1c levels in normal weight diabetics. No other subpopulations reported significant relationships between LBP and HbA1c. LBP was also significantly associated with BMI for normal weight diabetics and also for obese subjects regardless of their DM status.Conclusions
LBP is significantly related to DM status, but this relationship is complex and may interact with BMI. These results support the concept that LBP may be improved in normal weight diabetic subjects with improved glycemic control and weight loss, and that all obese LBP subjects may benefit from improved weight loss alone. 相似文献12.
Clinical usefulness of intraductal ultrasonography for the management of acute biliary pancreatitis 下载免费PDF全文
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Su Jeong Seong Jin Pyo Hong Bong-Jin Hahm Hong Jin Jeon Jee Hoon Sohn Jun Young Lee Maeng Je Cho 《Journal of Korean medical science》2015,30(11):1675-1681
While decreasing trend in gender differences in alcohol use disorders was reported in Western countries, the change in Asian countries is unknown. This study aims to explore the shifts in gender difference in alcohol abuse (AA) and dependence (AD) in Korea. We compared the data from two nation-wide community surveys to evaluate gender differences in lifetime AA and AD by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Face-to-face interviews using the Composite International Diagnostic Interview (CIDI) were applied to all subjects in 2001 (n=6,220) and 2011 (n=6,022). Male-to-female ratio of odds was decreased from 6.41 (95% CI, 4.81-8.54) to 4.37 (95% CI, 3.35-5.71) for AA and from 3.75 (95% CI, 2.96-4.75) to 2.40 (95% CI, 1.80-3.19) for AD. Among those aged 18-29, gender gap even became statistically insignificant for AA (OR, 1.59; 95% CI, 0.97-2.63) and AD (OR, 1.18; 95% CI, 0.80-2.41) in 2011. Men generally showed decreased odds for AD (0.55; 95% CI, 0.45-0.67) and women aged 30-39 showed increased odds for AA (2.13; 95% CI 1.18-3.84) in 2011 compared to 2001. Decreased AD in men and increased AA in women seem to contribute to the decrease of gender gap. Increased risk for AA in young women suggests needs for interventions. 相似文献
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脑部胶质瘤是临床中常见的一种原发性脑肿瘤,具有复发率高、死亡率高以及治愈率低的特点。常规临床诊断主要依靠计算机断层扫描(CT)和磁共振成像(MRI)检查技术进行鉴别。随着成像技术和机器学习方法的不断发展,多模态影像智能分析技术已经逐步成为研究热点,在脑胶质瘤的病灶分割测量、肿瘤分级、预后生存周期预测和基因型辨别等方面具有重要的应用前景。本文重点介绍基于机器学习和多模态影像在脑胶质瘤临床辅助诊断和预后评估中的应用进展。 相似文献
20.
James I. Geller MD Joseph G. Pressey MD Malcolm A. Smith MD Rachel A. Kudgus PhD Mariana Cajaiba MD Joel M. Reid PhD David Hall PhD Donald A. Barkauskas PhD Stephen D. Voss MD Steve Y. Cho MD Stacey L. Berg MD Jeffrey S. Dome MD PhD Elizabeth Fox MD Brenda J. Weigel MD 《Cancer》2020,126(24):5303-5310