Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…The fluctuations in the actual VD furnace refining endpoint molten steel temperature similarly impact the prediction errors of the MM–BO–XGBoost model and the other three existing models. …”
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Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population by Yu-Xin Guo, Jun-Long Lan, Wen-Qing Bu, Yu Tang, Di Wu, Hui Yang, Jia-Chen Ren, Yu-Xuan Song, Hong-Ying Yue, Yu-Cheng Guo, Hao-Tian Meng

“…The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions. …”
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Rifampicin-resistant Mycobacterium tuberculosis and unsuccessful results from Xpert® MTB/Rif-Ultra assay in Amhara Region, Ethiopia by Gizeaddis Belay, Hailu Getachew, Tigist Birku, Aimro Tadese, Yosef Gashaw, Michael Getie, Tazeb Molla, Molalign Tarekegn, Daniel Mekonnen, Alemayehu Abate

“…However, this rapid technology has inherent limitations, such as error reports, invalid results, and no results collectively reported as unsuccessful tuberculosis results. …”
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Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration by Heike Helmholz, Redon Resuli, Marius Tacke, Jalil Nourisa, Sven Tomforde, Roland Aydin, Regine Willumeit-Römer, Berit Zeller-Plumhoff

“…Using these machine learning methods, we were able to predict the proliferation of HUVECs for missing Mg concentrations and for missing passages with mean absolute errors below 10 % and as low as 8.5 %, respectively. …”
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Digital Health Intervention for Patient Monitoring in Immune-Mediated Inflammatory Diseases: Cocreation and Feasibility Study of the IMIDoc Platform by Diego Benavent, Jose M Iniesta-Chamorro, Marta Novella-Navarro, Miguel Pérez-Martínez, Nuria Martínez-Sánchez, Mónica Kaffati, Manuel Juárez-García, Marina Molinari-Pérez, Andrea González-Torbay, Mariana Gutiérrez, Natalia López-Juanes, Victoria Navarro-Compán, Irene Monjo-Henry, Germán Rodríguez-Rosales, Javier Bachiller, Enrique Calvo-Aranda, Xabier Michelena, Laura Berbel-Arcobé, Alejandro Balsa, Enrique J Gómez, Chamaida Plasencia-Rodríguez

“…ResultsDuring the feasibility testing, 111 entries were logged for the patient mobile app, comprising 76 errors identified and corrected, 16 improvements addressing functionality, usability, and performance, and 10 evolutionary suggestions. …”
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Patient safety culture through the perspectives of healthcare workers: a longitudinal study in a private healthcare network in Brazil by Glenio B Mizubuti, Saullo Queiroz Silveira, Leopoldo Muniz da Silva, Helidea de Oliveira Lima, Ana Claudia Lopes Fernandes de Araújo, Vanessa de Melo Silva Torres, Leandro Reis Tavares, Deborah Simões, Anthony MH Ho, Joaquim Edson Vieira

“…Key challenges identified in enhancing safety culture included communication openness, staffing, frequency of reported events, and nonpunitive response to errors.…”
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Advanced AI techniques for classifying Alzheimer’s disease and mild cognitive impairment by Sophie Tascedda, Sophie Tascedda, Sophie Tascedda, Pierfrancesco Sarti, Pierfrancesco Sarti, Veronica Rivi, Claudia Savia Guerrera, Giuseppe Alessio Platania, Mario Santagati, Filippo Caraci, Filippo Caraci, Johanna M. C. Blom, Johanna M. C. Blom

“…By deploying these advanced computational techniques, clinicians could see a reduction in diagnostic errors, facilitating earlier, more precise interventions, and likely to lead to significantly improved outcomes for patients.…”
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Agreement of glomerular filtration rate estimation equations for chemotherapy dosing in cancer patients at a tertiary referral hospital in Sub-Saharan Africa. by Wubshet Jote Tolossa, Tigist Workneh Leulseged, Abdu Adem, Feyissa Challa, Tirumebet Mezgebu, Ruth S Aytehgeza, Nebiat Adane Mera, Kalsidagn Girma Asfaw, Momina M Ahmed, Kebede H Begna

“…To assess the level of agreement, bias (mean error/ME), precision, and accuracy (root-mean squared error/ RMSE) were analyzed for each equation, where for all measurements a value closer to 0 indicates minimal bias, high precision, and high accuracy demonstrating good agreement with Cockcroft-Gault. …”
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Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization by Senyefia Bosson-Amedenu, Emmanuel Ayitey, Francis Ayiah-Mensah, Luyton Asare

“…Model performance was evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) metrics across three sample sizes (25%, 50%, and 75%). …”
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Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach by Jing Peng, Yan Zhang, Mengyu Zheng, Yanyan Wu, Guizhen Deng, Jun Lyu, Jianming Chen

“…Changes of U1-SN, LI-MP, Z angle and facial convex angle were set as continuous outcomes, mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²) were used as evaluation index. …”
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Bone Age Estimation of Chinese Han Adolescents’s and Children’s Elbow Joint X-rays Based on Multiple Deep Convolutional Neural Network Models by LI Dan-yang, ZHOU Hui-ming, WAN Lei, LIU Tai-ang, LI Yuan-zhe, WANG Mao-wen, WANG Ya-hui

“…Model performance was evaluated by comparing the mean absolute error （MAE）, root mean square error （RMSE）, accuracies within ±0.7 years （P±0.7 years） and ±1.0 years （P±1.0 years） between the estimated age and the actual age, and by drawing radar charts, scatter plots, and heatmaps.ResultsWhen segmented with Scheme 3, the UNet++ model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8% at a learning rate of 0.000 1. …”
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A nomogram for predicting early bacterial infection after liver transplantation: a retrospective study by Jie Yu, Jie Yu, Jichang Jiang, Jichang Jiang, Caili Fan, Caili Fan, Jinlong Huo, Jinlong Huo, Tingting Luo, Tingting Luo, Lijin Zhao, Lijin Zhao

“…These results indicate that the developed model exhibits good predictive performance and a moderate error in training and validation.ConclusionThe nomogram constructed in this study showed good differentiation, calibration, and clinical applicability. …”
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Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…Specifically, the root mean square error (RMSE) for the optimal MLP model, which utilized a learning rate of 0.001 and a momentum of 0.67, was 0.10. …”
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Impact of SGLT2 inhibition on markers of reverse cardiac remodelling in heart failure: Systematic review and meta‐analysis by Patrick Savage, Chris Watson, Jaimie Coburn, Brian Cox, Michael Shahmohammadi, David Grieve, Lana Dixon

“…The mean difference and standard error were extracted from each study and a random effects model used pool the mean difference and standard error across studies. …”
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Calibration and Verification of the AquaCrop Model in Simulating the Yield, Dry Matter and Water Productivity of Grain Corn (Zea mays L.) under Different Irrigation Methods and Nit... by zhaleh zarei, Hassan Heidari, Saeid Jalali Honarmand, Ali Bafkar

“…For statistical evaluation, root mean square error (RMSE), efficiency coefficient of the Nash-Sutcliffe model (EF), and Wilmot agreement index (d) were used.Results and DiscussionThe results indicated that the root mean square error (RMSE) for simulating canopy cover development under various irrigation and fertilizer treatments ranged from 1.5% to 6.1% during the calibration stage (2020) and from 2% to 6.4% during the verification stage (2021). …”
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First Results of a Randomized Controlled Trial of Hemoblock in Patients with Large Incisional Hernias by E. N. Degovtsov, P. V. Kolyadko, V. P. Kolyadko, A. V. Satinov

“…Design of a simple blind randomized controlled trial with a 90 percent study power, α-error equal to 0.05 and β-error equal to 0.10. For this purpose, the total number of subjects is planned to be 66. …”
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Impact of microbiological molecular methodologies on adaptive sampling using nanopore sequencing in metagenomic studies by Josephine Herbert, Stanley Thompson, Angela H. Beckett, Samuel C. Robson

“…We next compared the quality and accuracy of metagenomic analyses for two nanopore-based ligation chemistry kits with differing levels of base-calling error; the older and more error-prone (~ 97% accuracy) LSK109 chemistry, and newer more accurate (~ 99% accuracy) LSK112 Q20 + chemistry. …”
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Knee pain in runners: the most common causes, symptoms, and treatment review by Natalia Gizińska, Aleksandra Górniak, Amelia Rusiecka, Aleksandra Kubas, Paulina Lewandowska, Aleksander Sobczyk, Michał Widawski

“…Articles include randomized controlled trials, meta-analyses, and systematic reviews prioritizing resources published after 2015. Conclusions: Knee injuries in runners are multifactorial and often result from poor biomechanics, muscle imbalances, training errors, or repetitive strain. …”
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Application of quality control circle activities to improve the implementation of“one-doctor-one-patient”in the outpatient department of an ophthalmology hospital (运用品管圈提高眼病专科医院门诊“... by LAN Yanfei (兰艳飞), FAN Tianli (樊田莉), XING Xiaoying (邢晓英), ZHANG Yu (张瑜), ZHAO Yan (赵燕), LI Wenjing (李文婧)

“…The current situation and factors of errors in the implementation of “one-doctor-one-patient” in the outpatient department were analyzed. …”
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The current situation and associated factors of the psychological capital of nurses’ second victims in China: a cross-sectional study by Zhuoqing Deng, Qianfei Li, Hongyan Zan, Xiaohong Zhang, Ya‘nan Zhang, Zhiheng Gao

“…From the level of patient safety incidents, we should focus on the nurses with high event level and drug administration errors, and provide timely intervention support after the incident, so as to improve the psychological capital level of the second victims. …”
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