Spousal Concordance in Overweight and Obesity among Indian Couples: A Nationwide Analysis of Socioeconomic and Dietary Determinants by Prashant Kumar Singh, Lucky Singh, Shashi Kala Saroj, Chandan Kumar, Shekhar Kashyap, Shalini Singh

“…Understanding the shared risk factors within couples is crucial for effective intervention. …”
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Effect of deterioration of swallowing functions on the frailty status in older adults: a longitudinal cohort study by En-Chih Weng, Yoko Hasegawa, Tasuku Yoshimoto, Ma. Therese Sta. Maria, Koutatsu Nagai, Naoko Sato, Kana Tokumoto, Kazuhiro Hori, Hiroshi Kusunoki, Hiromitsu Kishimoto, Ken Shinmura

“…Conclusion This study found a significant association between frailty and multiple factors, particularly oral function. Specifically, diminished swallowing function has emerged as a notable independent predictor of frailty progression.…”
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Prevalence of COVID-19-associated pneumonia signs on chest computed tomography in cancer patients: the ARILUS study by A. A. Dyachenko, A. M. Grjibovski, M. A. Bogdanov, D. V. Bogdanov, E. A. Nazarova, A. A. Meldo, V. Yu. Chernina, M. G. Belyaev, V. A. Gombolevsky, M. Yu. Valkov

“…Using multivariate Poisson regression, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were calculated.Results. …”
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Artificial intelligence technologies in predicting genetic disorders and in personalized primary and secondary prevention of brain infarction in young people by T. I. Dutova, I. N. Banin, N. A. Ermolenko

“…The initial examination of patients with BI can play a principal role in the early identification of the factors that prognostically influence the pathology development. …”
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Dual-region MRI radiomic analysis indicates increased risk in high-risk breast lesions: bridging intratumoral and peritumoral radiomics for precision decision-making by Yuting Yang, Tingting Liao, Xiao-Hui Lin, Rushan Ouyang, Qiu Chen, Jie Ma

“…The intratumoral, each peritumoral, and the combined intratumoral and peritumoral radiomic models were established respectively. Independent risk factors predictive of malignant upgrades in high-risk lesions were identified through univariate and multivariable logistic regression analyses, which were subsequently incorporated as clinical and imaging characteristics. …”
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Geriatric nutritional risk index and body composition dictate the prognosis of elderly patients with intrahepatic cholangiocarcinoma by Sheng Wang, Luo Cheng, Lei Dou, Yuanli Kuang, Yang Huang, Tao Wen, Lei Xiang, Wenyuan Xie, Cheng Zhang, Dewei Li, Hui Li

“…Multivariate analysis identified low GNRI (p = 0.009), sarcopenia (p = 0.020), visceral adiposity (p = 0.033), and intramuscular fat deposition (p = 0.036) as independent prognostic factors for OS and RFS. The nomogram, incorporating GNRI, SMI, VSR, MA, microvascular invasion (MVI), CA19-9 levels, and lymph node invasion, demonstrated superior prognostic performance compared to the TNM stage, with a C-index of 0.734 (OS) and 0.704 (RFS) and an AUC of 0.809 (OS) and 0.815 (RFS).ConclusionGNRI, sarcopenia, IMF deposition, and visceral adiposity independently predict mortality and tumor recurrence in elderly ICC patients. …”
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Soluble galectin-4 in colorectal cancer is associated with tumor progression, but is not a predictor of the disease outcome by Elena Y. Yakunina, Olga V. Kovaleva, Ruslan A. Zukov, Alexey N. Gratchev, Sergey S. Salamatin, Sofya O. Kochkina, Zaman Z. Mammadli, Nikolay E. Kushlinskii

“…The galectin-4 levels were higher at advanced stages of the disease (p = 0.002) and in the presence of distant metastases (p 0.0001). Cox regression analysis showed that tumor size and distant metastases were independent prognostic factors (hazard ratio (HR) = 3.87; p = 0.0439; HR = 720.4; p = 0.0062, respectively) associated with decreased survival. …”
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From helicobacter pylori to glucose metabolism: can DOB values serve as a predictive marker? by Yu Zhou, Jie Xu, Zhigang Huang, Daoxing He, Hui Duan, Yating Shi, Zhenjie Wang, Zhaoyi Chen

“…Patients were stratified by DOB values, and metabolic abnormalities were defined by predefined criteria. Logistic regression analyzed the relationship between H. pylori status and metabolic parameters, adjusting for confounders. …”
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Hormonal determinants of prehypertension in a random sample of St. Petersburg residents: data from the ESSE-RF study by A. M. Erina, M. A. Boyarinova, E. V. Moguchaya, E. P. Kolesova, E. Yu. Vasilyeva, A. S. Alieva, N. A. Paskar, O. P. Rotar, E. I. Baranova, A. O. Konradi, E. V. Shlyakhto

“…To determine the association of prehypertension (PHTN) with cardiometabolic and hormonal factors in a population sample of St. Petersburg residents.Material and methods. …”
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Epidemiology of neonatal disorders attributable to low birthweight-global burden of disease research, 1990–2021 by Qi Zhang, Lan Luo, Lai-lai Yan, Lai-lai Yan, Jing Guo, Hui-jun Wu, Zi-wei Zhang, Yu-hua Zhu, Rui Qiao, Rui Qiao

“…Trends and influencing factors were investigated through Joinpoint regression, health inequality analysis, and frontier modeling.ResultsIn 2021, the global age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year (DALY) rate (ASDR) for neonatal diseases associated with low birth weight (LBW) were 22.76 [95% uncertainty interval (UI): 19.63–26.40] and 2,227.54 (95% UI: 1,939.96–2,563.52) per 100,000, respectively. …”
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The response to anti-seizure medications and the development of pharmacoresistant epilepsy in malformations of cortical development by Pu Miao, Meiping Ying, Ruotong Chen, Yuyu Yang, Yao Ding, Junming Zhu, Jianhua Feng, Jin Wang, Thandar Aung, Shuang Wang, Bo Jin

“…The clinical features of group I and group II were compared. Factors associated with PRE were analyzed. The time to development of PRE with different ASMs was assessed using Kaplan–Meier survival analysis. …”
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Association between the life’s essential 8 health behaviors score and all-cause mortality in cardiovascular-kidney-metabolic syndrome patients by Dingyuan Tu, Dingyuan Tu, Xiaoli Zuo, Ping Li, Jianming Wang

“…LE8 score, which included four health behaviors and four health factors, was categorized into low (0–49), moderate (50–79), and high (80–100) CVH. …”
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The association between chronic underlying diseases and the clinical characteristics in adult patients infected with Omicron BA.1 by Jingyu Wang, Yuechuan Cui, Henan Dong, Jie Zhao, Meng Wang, Chunlei Zhou, Hong Mu

“…Multivariable logistic regression results indicate that CUDs and advanced age (≥60 years old) are unfavorable factors for the increase in the severity of Omicron BA.1 infection [age (≥60 years old), OR = 2.96, 95 % CI: 1.35–6.50, P = 0.01; CUDs, OR = 2.78, 95 % CI: 1.65–4.70, P < 0.001]. …”
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Characteristics of patients with idiopathic normal pressure hydrocephalus for whom neurosurgeons hesitate to perform shunt surgery: a nationwide hospital-based survey in Japan by Ryo Kawai, Hiroaki Kazui, Tetsuya Ueba, Natsuko Nakamura, Marina Minami, Madoka Nakajima, Shigeki Yamada, Haruhiko Kishima, Hideki Kanemoto, Chifumi Iseki, Etsuro Mori

“…Responses were collected through December 11, 2023 and analyzed to assess current practices and factors influencing surgical decisions. …”
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Electrocardiographic findings for predicting the left anterior descending artery chronic total occlusion in patients with inferior ST-segment elevation myocardial infarction by Xiaobin Zheng, Zhaofu Zhang, Bingqi Yao, Haiyan Wu

“…Multivariate logistic regression analysis indicated that QRS V4 (OR = 1.062, P = 0.003), ST V4↓- ST III↑ (OR = 1.641, P = 0.050), and Killip classification (OR = 2.115, P = 0.004) were all independent risk factors for LAD occlusion. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

“…The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.ResultsXGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. …”
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Exploring the correlation between spatial heterogeneity of urban tourism and the degree of crowd gathering: Take the main area of Wuhan, China as an example by Xincan Yu, Hong Xu, Yuelin Yan

“…Using the Geographically Weighted Regression model, it analyzes the factors influencing tourism spatial heterogeneity. …”
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Spatio-Temporal Analysis of the Redundancies of Construction Land in the Beijing-Tianjin-Hebei Region (2000–2020) by Ting Zhang, Rui Shen, Yongqing Xie, Haowen Gao, Weitong Lv

“…The study employs the SBM-DEA model, Markov transfer probability matrix analysis, and multiple regression analysis to analyze the spatial change characteristics, spatial differentiation, and influencing factors of construction land redundancy in this Beijing-Tianjin-Hebei county unit during the period of 2000–2020. …”
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Propagandizing the treatise of wealth redistribution: an SMME story by Mathew E. Egu, Evelyn G. Chiloane-Tsoka

“…We employed an ordinary least squares (OLS) regression model technique for analyzing relevant secondary datasets using the free EViews 12 student version (SV) x64 statistical software analytical package. …”
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Longitudinal trajectories of symptom change during antidepressant treatment among managed care patients with depression and anxiety by Judith Cukor, Zhenxing Xu, Veer Vekaria, Fei Wang, Mark Olfson, Samprit Banerjee, Gregory Simon, George Alexopoulos, Jyotishman Pathak

“…This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. …”
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