Unveiling Patient Satisfaction: Analysis of Factor Influencing Patient-Centered Care At Bunda Margonda Hospital by Putu Agus Sujana Putra, NLP Dina Susanti, Made Rismawan, Made Dian Shanti Kusuma

“…In the non-BPJS group, age (p = 0.029) and marital status (p = 0.024) were significant factors. Logistic regression analysis revealed that length of stay was the most influential factor for BPJS patients (aOR = 2.3; p = 0.023), whereas marital status was the primary factor for non-BPJS patients (aOR = 1.3; p = 0.024). …”
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Predictive value of shear wave elastography and molecular subtypes for postoperative efficacy in patients with early breast cancer by Qiao-Ying Zhao, Bing Zhao, Gang Wu

“…Logistic regression analysis identified the elastic modulus value (odds ratio [OR]: 5.841 [3.714–6.112], p < 0.05), Ki-67 level (OR: 3.522 [1.865–3.897], p < 0.05) and molecular subtypes (OR: 4.331 [2.552–6.714], p < 0.05) as risk factors; logistic regression analysis also performed favourably for the prediction of pCR (area under the curve: 0.922 [0.871–0.934]) in patients with BC.Conclusion Elastic modulus value, Ki-67 level and molecular subtypes are risk factors for pCR in patients with BC.…”
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Effects of Scrambler Therapy in Patients with Failed Back Surgery Syndromes and Factors Associated with Depression Affecting Pain before and after the Therapy by Hayoung Byun, Min-Kyun Oh, Chang Han Lee

“…Spearman correlation analysis and Fisher’s exact test were used to confirm the correlation between BDI and other factors. Multiple regression analysis was used to identify independent factors predicting residual pain, posttherapy ODI, and posttherapy BPI. …”
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The higher serum uric acid to high-density lipoprotein cholesterol ratio is associated with increased risk of chronic obstructive pulmonary disease: result from NHANES 2011–2018 by Yunjia Bao, Jiangyuan Li, Zhiyu Wang, Linxi Lv, Mingyang Zhu

“…After adjusting for interfering factors, logistic regression analysis revealed a notable correlation between UHR and COPD. …”
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Effectiveness of sarcopenia screening indicators in predicting death in older patients with chronic heart failure by Zhouyu Li, Sha Huang, Xia Lin, Yukuan Xie, Yan Xia, Xiaoyan Chen

“…After adjustment for possible confounding factors, Cox regression analysis showed that compared with patients with PLR<125.11, those with PLR ≥ 125.11 had a higher risk of death within 30 days after discharge (HR = 3.75, 95% CI:1.09–12.94) as well as overall mortality (HR=1.82, 95%CI: 1.14–2.93). …”
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Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery by Yongjuan Bi, Yanhua Zhang, Li Li, Jing Bai, Jing Li

“…Fourteen risk factors could cause increased risk of PPH, including demographic characteristics and placental anomalies factors. Logistic regression analysis was used to influence the risk factors of PPH in vaginal delivery. …”
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Analysis of breastfeeding status and influencing factors among healthcare workers in Southwestern China by Tingting Dai, Jiaxin Wan, Qiuyue Liu, Xiaoyan Liu, Cheng Chen

“…The primary sources of breastfeeding knowledge were the internet and professional consultations, while common barriers included nipple/breast problems and work-related factors. Logistic regression analysis identified four independent factors associated with the continuation of exclusive breastfeeding for 6 months (all p < 0.05): parity (OR 1.86; 95%CI 1.35, 2.58), breastfeeding consultations during pregnancy (OR 1.44; 95%CI 1.04, 2.00), three-shift work resumption (OR 1.67; 95%CI 1.32, 2.10), and family support for breastfeeding (OR 1.90; 95%CI 1.07, 3.37). …”
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Association of Robson Ten Group Classification System with neonatal/postneonatal mortality: an analysis for the effect of the mass migrationAJOG Global Reports at a Glance by Damla Çarkçı Yıldız, MD, Elif Gül Yapar Eyi, MD

“…Robson Group Distribution: The distribution of cesarean births across Robson groups was varied, with the highest mortality rates observed in groups R8, R9, R10, R6, and R7 (p=.001). Risk Factors: Logistic regression analysis identified Robson groups, fetal presentation, gestational age, Apgar scores, and newborn weight/height as significant risk factors for neonatal /postneonatal mortality. …”
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Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques by Mohammed A. Mamun, Firoj Al-Mamun, Md Emran Hasan, Nitai Roy, Moneerah Mohammad ALmerab, David Gozal, Md. Shakhaoat Hossain

“…Methods Data from 1,485 participants were collected on socio-demographic characteristics, admission-related variables, health behaviors, and familial factors. Logistic regression analysis identified significant risk factors, while Machine Learning algorithms, including CatBoost and K-Nearest Neighbors, were used for prediction. …”
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Back to the basics–risk factor identification of pediatric malignant lymphadenopathy proven by pathological studies by Wei-Hsiang Su, Cheng-Che Wu, Chia-Man Chou, Sheng-Yang Huang, Hou-Chuan Chen

“…Weight loss, fixed LAP, firm consistency, and serum lactate dehydrogenase (LDH) exceeding 240 U/L were more related to malignant LAP than other risk factors. Multiple regression analysis revealed two independent risk factors. …”
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Association of pregnancy outcomes with neonatal TSH levels in euthyroid singleton pregnancies by Weiwei Feng, Weiwei Feng, Mengfan Song, Mengfan Song, Yu Meng, Yu Meng

“…After adjusting for confounding factors, multivariate regression analysis showed that the following pregnancy outcomes remained strongly associated with neonatal TSH elevation (P&lt;0.05): preeclampsia (OR=2.238, 95% CI 1.454 ~ 3.446), cesarean section (OR=1.404, 95% CI 1.179 ~ 1.672), advanced maternal age (OR=1.322, 95% CI 1.012 ~ 1.728), and preterm birth (OR=2.408, 95% CI 1.683 ~ 3.445).ConclusionNeonatal TSH levels are influenced by factors such as advanced maternal age, preeclampsia, cesarean delivery, and preterm birth, which can lead to elevated TSH in newborns.…”
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Cross-sectional study on the association between 24-hour urinary potassium excretion and the risk of H-type hypertension and non-H-type hypertension in Chinese adults by Qingfang He, Xiaofu Du, Xiangyu Chen, Lixin Wang, Yujia Fang, Jieming Zhong

“…After controlling for confounding factors, logistic regression analysis displayed that for every 1 mmol/L increase in 24-h urinary potassium excretion, the risk of H-type hypertension and non-H-type hypertension decreased by 1.4% (OR=0.986, 95% CI: 0.978-0.995, P=0.002) and 1.7% (OR=0.983, 95% CI: 0.968-0.998, P=0.025), respectively. …”
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Systemic immune-inflammatory index mediates the association between regular walking and depression in the elderly with coronary heart disease by Yanmin Ju, Xuncan Liu, Liang Zhao, Xixi Xie, Haoyue Jin, Jinying Chi, Songbai Miao, Zhenlan Li

“…After adjusting for confounding factors, linear regression analysis was used to assess the correlation among exercise-related factors, ln(SII) and depression. …”
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Effects of combined drip irrigation and mulching practices on the soil evaporation characteristics in a young apple orchard in arid northwestern China by Xinyu WANG, Haijing WANG, Xiao LI, Di WANG

“…Compared to the TL treatment, the daily SE under FM and SM treatments was more susceptible to meteorological factors. Stepwise regression analysis showed that the daily SE of the young orchard was mainly controlled by the vapor pressure deficit, reference evapotranspiration and solar radiation, regardless of the treatment. …”
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Risk factors of catheter-related blood stream infection associated with chronic hemodialysis diabetics with cuffed dialysis catheter in Baoding area by Li Xiao-dong, Li Xin-yue, Li Bao-xin, Wang Xiang-dong, Tang Guan-ying

“…P<0.05 was deemed as statistically significant and multi-factor Logistic regression analysis was performed. Results There were 26 cases of CRBSI with an incidence rate of 2.4 thousand duct day; 29 strains of pathogenic bacteria were detected with predominantly Staphylococcus aureus（34.48%）,followed by Staphylococcus epidermidis and Pseudomonas aeruginosa（13.79%）. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. …”
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Modeling and statistical analysis of the impact of environmental factors on aquatic biodiversity by Kozlova Anastasia, Solovieva Tatiana, Kurashkin Sergei, Bricotnin Maxim, Kandaurova Natalia

“…The work employed correlation, factor, and regression analysis methods, which revealed significant relationships between ecological characteristics and amphibian species abundance. …”
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From taste to recommendation: An analysis of factors shaping eWOM at gastronomic events in Vojvodina (R. Serbia) by Vukolić Dragan, Gajić Tamara, Milošević Srđan

“…Data were analysed using factor and regression analysis. The findings indicate a positive correlation between various factors, including gastronomic motivation, social and emotional experiences, pre-visit information-seeking behaviour, post-visit evaluation, and behavioural outcomes related to eWOM. …”
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Internet addiction and its influencing factors among middle school students in Hunan province: a cross-sectional study by Fei WANG, Lixi QIN, Zhuoga NIMA, Yaqing TAN, Miyang LUO, Yanhua CHEN, Jiayou LUO

“…The chi-square test was used to compare the differences in internet addiction detection rates between groups, and multi-factor logistic regression analysis was used to analyze the influencing factors of internet addiction. …”
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PROJECT SUCCESS AND INDIVIDUAL ENTREPRENEURIAL ORIENTATION OF PROJECT MANAGERS: RUSSIAN CONTEXT by C. A. Titov, R. D. Pathak, A. A. Tsymbal

“…The collected data were examined using factor and regression analysis. The findings indicate that there is a negative relationship between the entrepreneurial nature and such indicators of project success as internal efficiency, preparation for the future, and results for clients. …”
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