Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning by Zhang ZL, Chen KJ, Chen H, Zhu MM, Gu JJ, Jiang LS, Zheng L, Zhou SG

“…This fear can have significant negative effects on both the mother and the newborn, making it crucial to study the influencing factors of FOC to implement early interventions.Objective: First, identify the risk factors for FOC occurrence, then construct a predictive model for FOC and evaluate its predictive efficiency.Methods: A total of 901 pregnant women who underwent regular prenatal check-ups at Anhui Women and Children’s Medical Center were selected. …”
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Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis by You Pu, Jin Yang, Lian Shui, Qianlong Tang, Xianqin Zhang, Guangguo Liu

“…The aim of this study was to systematically evaluate and analyze all predictive models used to predict dysphagia in patients with HNC after radiotherapy.MethodsPubMed, Cochrane Library, EMbase and Web of Science databases were searched from database establishment to August 31, 2024. …”
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Implementing a transmural model of early palliative care in advanced dementia: the use of a hybrid effectiveness-implementation study design by Allyn Hum, Palvinder Kaur, Wen Yang Goh, Riyin Tay, Han Yee Neo, Mervyn Yong Hwang Koh, Noorhazlina Binte Ali, Wee Shiong Lim, Yu-Ling Jackie Tan, Huei Yaw Wu, Palvannan Kannapiran, Hwee Teng Robyn Tan, Yan Sun, Chin Ee Ong, Ravinder Singh Sachdev, Zhi Jun Low, Lee Hung Tey, Woan Shin Tan, Yew Yoong Ding

“…Aim The aim of this study is to implement a transmural model of care in a tertiary care setting where patients living with advanced dementia (PLAD) at risk of deterioration in one year are identified early to receive tailored palliative care interventions using a predictive algorithm. Methods/Design The updated medical research council (MRC) framework for complex interventions is used to guide the development and implementation of the transmural model which incorporates a predictive algorithm in clinical practice, with interventions tailored for at risk PLAD both within, and beyond the tertiary care setting. …”
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Evaluation of a Streptococcal Pharyngitis Score in Southern Taiwan by Ching-Tang Shih, Ching-Chiang Lin, Chung-Ching Lu

“…The McIsaac sore throat score is a clinical prediction score used to improve the detection rate of GAS pharyngitis. …”
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Evaluation of inflammatory parameters in patients with hepatic hydatid disease by Zhijia Fan, Yao Hu, Li Wang, Haoqin Jiang, Dandan Li, Hui Zhao, Zhicheng Wang

“…Background To the best of our knowledge, the association of inflammatory parameters with hepatic hydatid disease (HD) has not been investigated in a single study. We aimed to evaluate the potential value of inflammatory indices in this disorder.Methods The retrospective study including 114 patients was performed from January 2016 to November 2019. …”
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Exploring the growth and impact of artificial intelligence in anesthesiology: a bibliometric study from 2004 to 2024 by Keke Liu, Weicheng Qiu, Xinping Yang

“…This bibliometric analysis aims to evaluate publication trends, key contributors, and emerging translational pathways in AI research in anesthesiology, with special emphasis on clinical relevance, thematic clustering, and future application prospects.Materials and methodsPublications related to AI in anesthesiology from 2004 to 2024 were retrieved from the Web of Science Core Collection database, resulting in 658 articles. …”
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Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery by Catherine Chiu, Matthias R. Braehler, Anne L. Donovan, Atul J. Butte, Romain Pirracchio, Andrew M. Bishara

“…We describe the development of a machine-learning derived model to predict UIAs using only widely used preoperative variables. …”
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Establishment and validation of a nomogram for predicting preterm birth in intrahepatic cholestasis during pregnancy: a retrospective study by Wenchi Xie, Landie Ji, Dan Luo, Lili Ye, Qian Li, Landan Kang, Qingquan He, Jie Mei

“…Abstract Objective This study aimed to develop and evaluate a nomogram for predicting preterm birth in patients with intrahepatic cholestasis of pregnancy (ICP), with a view to assisting clinical management and intervention. …”
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Prognostic Factors and Long-Term Outcome Prediction in Patients with Hypopharyngeal Carcinoma Treated with (Chemo)radiotherapy: Development of a Prognostic Model by Miloslav Pala, Pavla Novakova, Adam Tesar, Lucie Vesela, Antonin Vrana, Jarmila Sukova, Zdenka Pechacova, Petra Holeckova, Tereza Drbohlavova, Tomas Podlesak, Petra Tesarova

“…<b>Background/Objectives</b>: To evaluate the effectiveness of curative (chemo)radiotherapy in patients with hypopharyngeal carcinoma and to identify prognostic factors influencing treatment outcomes. …”
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Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative by Randall W. Grout, Mohammad Ateya, Baely DiRenzo, Sara Hart, Chase King, Joshua Rajkumar, Susan Sporrer, Asad Torabi, Todd A. Walroth, Richard J. Kovacs

“…This study evaluated a clinical workflow incorporating UNAFIED for screening, education, and follow-up evaluation of patients visiting a cardiology clinic who may be at an elevated risk of developing AF within 2 years. …”
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Early Changes in Clinical and Laboratory Parameters in Patients Died of COVID-19 by K. S. Bakhtiyarova, A. O. Papoyan, A. V. Alekseev, D. S. Vishnyakov, A. A. Grazhdankin, I. R. Gilyazova, A. Kh. Nasibullina, M. V. Kislitsyna, A. A. Vakhitova, K. N. Zolotukhin, I. R. Kabirov, V. N. Pavlov

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Association between the Triglyceride-glucose index and fragility fractures among US adults: insights from NHANES by Yuan Lou, Huan Chen, Fuli Man, Lina Zhang, Qi Pan

“…RCS regression demonstrated a positive linear relationship between the TyG index and fragility fractures. The predictive nomogram, incorporating the TyG index and other clinical factors, demonstrated favorable predictive performance (consistency index = 0.901). …”
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A novel nomogram for predicting carotid atherosclerosis risks in diabetic patients by Jing Liu, Shoukun Dou, Yuan Zhang, Xiaoyun Zeng, Yingnan Kang, Qianna Xu, Xiangchen Liu, Ping Zhao

“…Methods A retrospective cohort study involving 1,258 patients with type 2 diabetes was conducted. …”
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Predictive Value of the Triglyceride-Glucose Index for Metabolic-Associated Fatty Liver Disease in Individuals with Different Metabolic Obese Phenotypes by Lv D, Wang Z, Liu H, Meng C

“…Dan Lv,1 Zepu Wang,2 Huanxin Liu,1 Cuiqiao Meng1 1Physical Examination Center, Hebei General Hospital, Shijiazhuang, Hebei, People’s Republic of China; 2Department of Hepatobiliary Surgery, Hebei General Hospital, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Dan Lv, Physical Examination Center, Hebei General Hospital, 348 heping West Road, Shijiazhuang, Hebei, People’s Republic of China, Email ld2449@163.comObjective: To investigate the relationship between triglyceride-glucose (TyG) index and metabolic-associated fatty liver disease (MAFLD), and to evaluate the predictive value of the TyG index for MAFLD in individuals with different metabolic obese phenotypes. …”
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Precision prognostic model for uveal melanoma: nomogram prediction and comparison with AJCC system by Wentao Wu, Jingjing Zhu, Yuzhu Wu, Daning Li, Rong Liu, Yuchen Zheng, Qiuqiong Deng, Yiqiang Yuan

“…This study aims to develop a nomogram model to predict the 3-year, 5-year, and 8-year overall survival (OS) of UM patients. …”
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Risk prediction model of physical frailty for a rural older population: a cross-sectional study in Hunan Province, China by Xiuyan Guo, Chunhong Shi

“…This study investigated the risk variables among rural older adults in Hunan Province, China, and developed a physical frailty prediction model to inform policymaking to enhance their health and well-being.MethodsThis study was conducted from July 22 to September 3, 2022. …”
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Development and validation of a perioperative risk prediction model for pressure ulcers in neurosurgical procedures: a machine learning approach with protocol compliance metrics by Yaping Wang, Weiguang Yu, Hui Zhi, Kun Shang, Hongmei Yin, Dandan Shan, Xiao Li, Wenxia Li, Xiuru Zhang, Baoli Zhang

“…BackgroundThis study aimed to develop and validate a nomogram for predicting pressure ulcer (PU) incidence in neurosurgical patients to enhance postoperative risk management.MethodsA retrospective analysis of 1,020 patients across four tertiary centers (2005–2025) evaluated 20 variables. …”
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The 30-min diaphragm movement change rate for predicting weaning success in severe pneumonia patients requiring invasive ventilation by Wentao Luo, Huagen Zhang, Yuchong Chen, Wenfeng Luo, Xiuwen Lin

“…PurposeThis study evaluated the 30-min diaphragm excursion change rate (ΔDE30–0) as a novel predictor of weaning success compared to existing parameters in patients with severe pneumonia requiring invasive mechanical ventilation.MethodsThis retrospective cohort study enrolled patients with severe pneumonia requiring invasive mechanical ventilation (n = 100). …”
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A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei by Dong Bai, Guanjun Shi, Yuanzi Liang, Fang Li, Zhuozhao Zheng, Zhiqun Wang

“…Materials and methods This retrospective study analyzed 158 pathologically confirmed PMP cases (85 low-grade, 73 high-grade) from January 4, 2015 to April 30, 2024. …”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

“…The XGBoost model has excellent predictive efficacy and clinical utility. The SHAP method was ranked according to the importance of the degree of influence on the model, with age, heart rate, Standard deviation of the NN intervals (SDNN), two nonlinear parameters of HRV and sex considered to be the top 6 predictors. …”
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