Validity and reliability of inertial measurement units for measuring gait kinematics in older adults across varying fall risk levels and walking speeds by Lulu Yin, Peng Chen, Junkai Xu, Yixue Gong, Yifang Zhuang, Yiming Chen, Lin Wang

“…The Coefficient of Multiple Correlation (CMC) was employed to assess waveform consistency, while systematic error (SE) was calculated to quantify deviations. …”
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Design of intelligent human-machine collaborative robot-assisted craniotomy system by Meng Cui, Wenqing Ren, Tengfei Cui, Ruifeng Chen, Yi Shan, Xiaodong Ma

“…Also, in mini pig experiments, “robot” group had less average positioning error (3.26 ± 0.51 mm VS 4.39 ± 0.75 mm, P = 0.008) and time spent (11.83 ± 0.92min VS 26.10 ± 1.62min, P < 0.001) compared with “surgeon” group. …”
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Adaptation and psychometric evaluation of the Chinese version of the hip disability and osteoarthritis outcome Score–Physical function Short-form (HOOS-PS) by Mengyuan He, Benjing Song, Hongbao Hu, Dongfa Liao, Lin Cui, Li Yin, Lin Wu, Amuyida, Shihong Li, Yingchao Tang, Jianxiang Long, Qingyun Xie, Wei Wang

“…The CFA results showed a good fit (root mean square error of approximation (RMSEA) = 0.050, the comparative fit index (CFI) = 0.995, and the Tucher-Lewis Index (TLI) value = 0.991). …”
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Functioning in patients with major depression treated with duloxetine or a selective serotonin reuptake inhibitor in East Asia by Novick D, Montgomery W, Haro JM, Moneta MV, Zhu G, Yue L, Hong J, Duenas H, Brugnoli R

“…At 24&amp;nbsp;weeks, the estimated mean SDS total score was 4.48 (standard error =0.80) in the duloxetine cohort, which was statistically significantly lower (ie, better functioning) than that of 6.76 (standard error =0.77) in the SSRI cohort (P&amp;lt;0.001). …”
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Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients by Xiaodong Chai, Hongxia Li, Moxuan Yang, Junyang Zeng, Genxiu Chen, Yongmeng Li, Wei Wang, Zichen Liu, Kun Li, Tongmei Zhang, Shuhao Wang, Nanying Che

“…Model performance was evaluated using distinct metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity for TR prediction, and mean absolute error (MAE), mean squared error (MSE), and Kaplan–Meier analysis for PFS time estimation. …”
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A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements by Zheng Zeng, Linkai Tao, Hangyu Zhu, Yunfeng Zhu, Long Meng, Jiahao Fan, Chen Chen, Wei Chen

“…Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from &#x2212;50&#x00B0; to &#x002B;50&#x00B0;. …”
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Impact of Best Corrected Final Visual Acuity on the Performance of Intraocular Lens Power Calculations by Shammas HJ, Shammas MC, Bahr C, Sahota R, Hall B

“…We evaluated the Mean Prediction Error (MPE) and its standard deviation (SD), the Mean Absolute Error (MAE) and its SD and the percentage of eyes within ± 0.50D and ± 1.00D in relation to BCFVA.Results: We analyzed 4 groups with BCFVA noted in LogMAR of ≤ 0.00, 0.02– 0.10, 0.12– 0.20 and 0.22– 0.30. …”
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Changes of Surface Wind Speed over Qinghai-Xizang Plateau from 1961 to 2020 and Evaluation of the Dynamical Downscaling Simulations by Jia WU, Jie WU, Yuping YAN

“…In the context of global warming， the in-depth study of the change in surface wind speed over Qinghai-Xizang Plateau can provide support for development planning for the double-carbon policy.Based on the multi-source datasets， it is expected to obtain the qualitative conclusion.In addition， to comprehensive evaluate the performance of CN05.1 gridded dataset as well as the dynamical downscaling simulations can provide guidance for the application of these two datasets over this region.Firstly， the monthly， seasonal and annual averaged wind speed， inter-annual， intra-annual variability of surface wind speed， the trends of wind speed probability for different grades， as well as the spatial distribution over Qinghai-Xizang Plateau were analyzed， based on the five wind speed datasets from 1961 to 2020.Then， the performance in reproducing the monthly， seasonal， annual averaged and long-term variability of surface wind speed from 1986 to 2005 was evaluated， using the five dynamical downscaling simulations and their ensemble mean from regional climate model （RegCM4）.The result indicated that decreasing trends were generally found in the seasonal and annual averaged surface wind speed from 1961 to 2002， with different magnitude among different datasets， while for the period from 2002 to 2020， the significant increasing trend was observed both in CN05.1 and NCEP-1.In addition， the probability of calm wind （0.0~0.2 m·s-1） showed a slightly decrease both in 1961 -2002 and 2002 -2020.The light air （0.3~1.5 m·s-1） and light breeze （1.6~3.3 m·s-1） increased in 1961 -2002 and decreased in 2002 -2020， in the meantime， the gentle breeze （3.4~5.4 m·s-1） and moderate breeze （5.5~7.9 m·s-1） showed a similar change to the mean surface wind speed.Furthermore， the wind speed greater than or equal to 8 m·s-1 mainly decreased in 1961 -2002 and 2002 -2020.Overall， the change in CN05.1 was more closed to station dataset and showed consistent characteristics with JRA-55， which behaved better among reanalysis datasets， indicating the high applicability of CN05.1 over Qinghai-Xizang Plateau.Although all downscaling simulations could reasonably capture the spatial distribution of surface wind speed in different seasons and also well reproduced the wind speed pattern among different topography， the underestimation existed in the northwestern part of Qinghai-Xizang Plateau while the overestimation was prevailing in the other region.Furthermore， the significant deviation was found in the simulation of the intra-annual change， and the magnitude of trends was also underestimated.In terms of different simulations， the result driven by HadGEM2-ES showed a better performance in reproducing the spatial distribution of seasonal and annual mean surface wind speed， while the result driven by MPI-ESM-MR showed smaller root mean square error and better trends.Overall， the smallest relative root mean square error was observed in ensemble mean， implying more stable ability than the individual simulation.…”
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VEGF Polymorphisms Related to Higher Serum Levels of Protein Identify Patients with Hepatocellular Carcinoma by Maria Eduarda Lopes Baitello, Graciele Domitila Tenani, Rafael Fernandes Ferreira, Victor Nogueira, Marcela Augusta de Souza Pinhel, Rita de Cássia Martins Alves da Silva, Renato Ferreira da Silva, Patrícia da Silva Fucuta, Moacir Fernandes de Godoy, Dorotéia Rossi Silva Souza

“…Polymorphisms were analysed by PCR/RFLP and serum levels of VEGF by ELISA. Alpha error was set at 5%. The wild-type genotype of both polymorphisms prevailed (P>0.05). …”
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Do Conditionals Have Truth Conditions? by Dorothy Edgington

“…Las posibilidades son, en efecto: A B Si A,B v v v f v v f v f (En el análisis de la primera línea se usa la conclusión de que la veritativo-funcionalidad no puede fallar en el caso verdad-verdad, conclusión ya establecida antes.) …”
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MRI Application in Quantification of Epiphyseal Development in the Wrist and Bone Age Estimation of Han Male Adolescents in East China by ZHOU Zhi-lu, ZHANG Dong-fei, CHEN Jie-min, WANG Ya-hui, HAO Hong-xia, LIU Tai-ang, HE Yu-heng, LONG Ding-nian, LIU Rui-jue, WAN Lei

“…Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.…”
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Effectiveness of Community Smoking Prevention Education in Enhancing Adolescent’s Knowledge in Banyuwangi Indonesia by Azimatul Karimah, Soetjipto, Izzatul Fithriyah, Sheila Maryam Gautama, Agustina Sjenny, Adila Taufik Syamlan, Nindy Adhilah, Dina Faizatur Rahmah, Williana Suwirman, Virzi Aliyyah Rahma, Teisha Jediya Videlia Marantika, Rifat Nurwita Kusumaningtyas, Andyani Pratiwi, Rizka Solehah

“…Eventually, the standard error mean for the pre-test and post-test is 2.073. …”
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Analysis of the Characteristics and Contribution Rate of Base Flow in Typical Basins of the Loess Plateau by ZHANG Jiyuan, YU Kunxia, LI Zhanbin, LI Peng, LIU Yonggang, MO Shuhong, ZHAO Binhua, YANG Jianhong, JIA Lu, LI Xue

“…[Results] The F4 digital filtering method exhibited superior performance in terms of stability, dispersion, and error metrics, making it the most suitable method for the typical watersheds of the Loess Plateau. …”
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Cross-cultural adaptation and validation of the Chinese version of the taste and smell survey for cancer patients by Haixin Liang, Qing Yang, Li Yin, Huarong Pang, Enmei Jiu, Bin Li, Lei Qin

“…Conclusion The study has confirmed that the Chinese version of the TSS is a concise and effective tool that is easy to administer and demonstrates strong scientific quality, reliability, validity, and feasibility. …”
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Toward a Self-Inflating Bag Mechanical Ventilator, Maturation in Continuous Use, Safety, and Repeatability for Ventilatory Variables by Noe Rodriguez, Carlos Rubio, Leonardo Barriga, Fernando Fonseca, Jorge Soto, Jose Pineda, David Sanchez, Antonio Rojas, David Lopez, Dalia Madrigal, Blanca Villagran, Mayra Sosa, Silvio Ñamendys, Alan Gutierrez, Alain Cueto

“…Six pigs were assigned to the experimental group and six were assigned to the reference group.Results The EHT presented a relative error within the allowed margin for both pressure and volume modes. …”
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A nomogram clinical model for predicting preoperative lower limb venous thrombosis in knee ligament injuries: a retrospective study by Jincai Duan, Jincai Duan, Tianjie Xiao, Tianyou Xing, Tianyou Xing, Wei Qin, Wei Qin, Zhihui Wang, Zhihui Wang, Haoduan Dou, Haoduan Dou, Di Wu, Yuanliang Du, Yuanliang Du

“…The ROC showed good diagnostic efficacy, with an area under the curve (AUC) of 0.888, and the calibration curves showed good agreement (mean absolute error = 0.013).ConclusionGender, damaged ligament site, and D-dimer level can be used as independent risk factors for the preoperative prediction of LLVT, and the nomogram model proposed in this study can better assist clinicians in making clinical decisions.…”
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Clinical performance review for 3-D Deep Learning segmentation of stroke infarct from diffusion-weighted images by Freda Werdiger, Vignan Yogendrakumar, Milanka Visser, James Kolacz, Christina Lam, Mitchell Hill, Chushuang Chen, Mark W. Parsons, Andrew Bivard

“…Clinical experts reviewed these models on the clinical test set, agreeing with the model if no 'significant’ error was present. The average agreement with the model and interrater agreement was also derived. …”
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Socioeconomic inequality in the co-occurrence of anemia and stunting among adolescent girls aged 15–19 years in Sub-Saharan African countries: a decomposition analysis by Tewodros Getaneh Alemu, Elsa Awoke Fentie, Daniel Gashaneh Belay, Desale Bihonegn Asmamaw, Ever Siyoum Shewarega, Wubshet Debebe Negash, Habitu Birhan Eshetu, Fantu Mamo Aragaw, Samrawit Mihret Fetene, Rediet Eristu Teklu

“…Results The weighted Erreygers normalized concentration index for co-occurrence of anemia and stunting was − 0.046 with Standard error = 0.0043 (P value < 0.0001); indicating that co-occurrence of anemia and stunting was disproportionately concentrated among the poor. …”
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Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development by Imanol Pinto, Álvaro Olazarán, David Jurío, Borja De la Osa, Miguel Sainz, Aritz Oscoz, Jerónimo Ballaz, Javier Gorricho, Javier Gorricho, Mikel Galar, Mikel Galar, José Andonegui, José Andonegui

“…An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93% of a sample of them as referable. …”
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Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants by Xiangjun Qi, Shujing Wang, Caishan Fang, Jie Jia, Lizhu Lin, Tianhui Yuan

“…LightGBM achieved the highest predictive accuracy at 87.9 %, a classification error rate of 12.1 %, and the top area under the receiver operating characteristic curve (0.951) and the precision‐recall curve (0.930). …”
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