Reaction time variations in normal aging and elderly MCI patients under various cognitive load conditions by Yanfei Zhu, Yanfei Zhu, Zhuoming Chen, Zhengkun Shi, Zhengkun Shi, Junyi Chen, Junyi Chen, Qiankun Zuo, Qiankun Zuo, Zhi Yang, Wenjing Zhang, Wanting Li, Siyu Lu, Siyuan Peng, Lei Gou

“…A positive linear correlation was found between the MoCA score and task accuracy rate (r = 0.758, P = 0.011).ConclusionDominant responses require less processing time, whereas tasks demanding interference suppression elicit slower reaction times and higher error rates. …”
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Assessing Social Participation Among Kidney Transplant Recipients Using PROMIS Computer Adaptive Testing by Maria G. Pucci, Mowa Ayibiowu, Jad Fadlallah, Aghna Wasim, Nathaniel Edwards, Madeline Li, Doris Howell, Susan Bartlett, John D. Peipert, Samantha Anthony, Istvan Mucsi, Janine Farragher

“…Reliability of the PROMIS-SP CAT was determined using standard error of measurement (SEM) and test-retest reliability using intraclass correlation coefficient. …”
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Validity and contributions to pain from the central aspects of pain questionnaire in rheumatoid arthritis by Stephanie Louise Smith, Vasileios Georgopoulos, Onosi Sylvia Ifesemen, Richard James, Eamonn Ferguson, Richard J. Wakefield, Deborah Wilson, Philip Buckley, Dorothy Platts, Susan Ledbury, Ernest Choy, Tim Pickles, Zoe Rutter-Locher, Bruce Kirkham, David Andrew Walsh, Daniel F. McWilliams

“…Central aspects of pain demonstrated acceptable reliability (ICC(2,1) = 0.71), CFA fit (comparative fit index = 0.99, Tucker–Lewis index = 0.99, root mean square error of approximation = 0.034, standardized root mean residuals = 0.03), and internal consistency (α = 0.82). …”
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Urinary metabolites as biomarkers of dietary intake: a systematic review by Mariah Kay Jackson, Bing Wang, Heather Rasmussen, Sathish Kumar Natarajan, Sathish Kumar Natarajan, Laura D. Bilek, Diane K. Ehlers, Laura Graeff-Armas, Christopher D’Angelo, Teresa Cochran, Kimberly Harp, Corrine Hanson

“…BackgroundCurrent diet assessment tools, such as food frequency questionnaires, may result in misclassification bias from measurement error and misreporting. These limitations can be mitigated by diet-related biomarkers in urine specimens, an emerging approach to characterize dietary intake.ObjectiveWe conducted a systematic review to identify urinary biomarkers with utility in accurately assessing dietary intake, including individual foods and food groups.MethodWe retrieved studies from 2000 to 2022 from databases including Embase, CINAHL, Cochrane, and PubMed. …”
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Habit-learning and decision-making circuits are susceptible to glycemic variability in type 2 diabetes: a longitudinal study by Carolina Moreno, Carolina Moreno, Carolina Moreno, Otília C. d’Almeida, Otília C. d’Almeida, Joana Crisóstomo, Nádia Canário, Leonor Gomes, Leonor Gomes, Leonor Gomes, Miguel Castelo-Branco, Miguel Castelo-Branco

“…Negative correlations between GV metrics and SPC volume of regions involved in habit-learning, decision-making, and memory highlight GV as a mediator of the neural impact of T2DM on the reward prediction-error circuits.…”
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Corrigendum: The accuracy and precision of CT-RSA in arthroplasty: a systematic review and meta-analysis by Sjors F van de Vusse, Nienke N de Laat, Lennard A Koster, Bart Kaptein

“…Before publication of De Laat (2024) we updated the values for the RMS error for accuracy in translations and rotations based on an increased number of observations. …”
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Evaluation of Race-Neutral Glomerular Filtration Rate Estimating Equations in an Indian Population by Ashok Kumar Yadav, Jaskiran Kaur, Prabhjot Kaur, Kajal Kamboj, Yoshinari Yasuda, Masaru Horio, Arnab Pal, Nusrat Shafiq, Nancy Sahni, Harbir Singh Kohli, Seiichi Matsuo, Vivek Kumar, Vivekanand Jha

“…Performance of eGFR equations (CKD-EPICr(2021), CKD-EPICr-Cys(2021), CKD-EPICr(2009), CKD-EPICr-Cys(2012), CKD-EPICys, 2020Csy-B2M-BTP and 2020Cr-Csy-B2M-BTP, EKFCcr, EKFCcys, and EKFCcr-cys) were assessed against measured GFR (mGFR) using bias, precision, and accuracy (root mean square error [RMSE], mean absolute error [MAE] and P30 [% with eGFR within 30% of mGFR]). …”
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Robotic Versus Open Pancreatoduodenectomy With Vein Resection and Reconstruction: A Propensity Score-Matched Analysis by Niccolò Napoli, MD, PhD, FEBS, Emanuele Federico Kauffmann, MD, PhD, FEBS, Michael Ginesini, MD, Armando Di Dato, MD, Virginia Viti, MD, Cesare Gianfaldoni, MD, Lucrezia Lami, MD, Carla Cappelli, MD, Maria Isabella Rotondo, MD, Daniela Campani, MD, Gabriella Amorese, MD, Caterina Vivaldi, MD, Silvia Cesario, MD, Laura Bernardini, MD, Enrico Vasile, MD, Fabio Vistoli, MD, PhD, FEBS, Ugo Boggi, MD, FEBS

“…To achieve a power of 80% (noninferiority margin:10%; α error: 0.05; ß error: 0.20), a 1:1 propensity score-matched analysis required 35 pairs. …”
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Transient simulation of multi-product pipeline driven by flow mechanisms and operational data by Jian DU, Haochong LI, Qi LIAO, Kaikai LU, Jianqin ZHENG, Xiao YU

“…Compared with the DNN model, the PINN model produced predictions for G1 pipeline pressures, with the mean absolute percentage error (MAPE) reduced by 77.4%, 88.7%, and 87.8%, respectively. …”
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Evaluating the psychometric measurement properties of patient-reported outcome measures for uterine fibroids using the Consensus-based Standards for the selection of health Measure... by Wei Yan, Fei Liu, Ya Li, Shixuan Wang, Minli Zhang, Wenwen Wang, Suzhen Yuan, Shuhao Yang, Qing Yan, Tao Xiang

“…However, four other PROMs, namely the Perioperative Anxiety Scale for Uterine Fibroids, Fibroid Symptom Diary, Uterine Fibroid Daily Bleeding Diary and Menstrual Pictogram Superabsorbent Polymer-containing Version 3, received class B recommendations (further research required) due to poor measurement properties, including inadequate reliability and unquantified measurement error.Conclusion The results of the present study fill a knowledge gap in the systematic and comprehensive evaluation of uterine fibroid-related PROMs. …”
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Improved epigenetic age prediction models by combining sex chromosome and autosomal markers by Zhong Wan, Peter Henneman, Huub C. J. Hoefsloot, Ate D. Kloosterman, Pernette J. Verschure

“…Results We employed random forest regression (RFR) to construct age prediction models with publicly available DNAm Infinium 450 K microarray data of sex chromosomes from human whole blood and buffy coat samples and assessed the RFR model performance based on the root-mean squared error (RMSE) and the mean absolute deviation (MAD) of cross-validation. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). …”
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Estudio sobre la idoneidad de dos test de valoración de la amplitud de movimiento en personas sedentarias con discapacidad intelectual (Study on the suitability of two test for th... by Ruth Cabeza-Ruiz, Pedro Tomás Gómez Piriz

“…Para conocer la idoneidad de ambos test, se calculó la fiabilidad, la viabilidad, el error estándar de la medida (EEM) y el mínimo cambio detectable (MCD). …”
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Validation of Deep Learning–Based Automatic Retinal Layer Segmentation Algorithms for Age-Related Macular Degeneration with 2 Spectral-Domain OCT Devices by Souvick Mukherjee, PhD, Tharindu De Silva, PhD, Cameron Duic, BS, Gopal Jayakar, BS, Tiarnan D.L. Keenan, BM BCh, PhD, Alisa T. Thavikulwat, MD, Emily Chew, MD, Catherine Cukras, MD, PhD

“…Main Outcome Measures: Performance metrics (including mean squared error, mean absolute error [MAE], and Dice coefficients) for the segmentations of the internal limiting membrane (ILM), retinal pigment epithelium (RPE), and RPE to Bruch’s membrane region, along with en face thickness maps, volumetric estimations (in mm3). …”
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Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study by Suzanne E Mitchell, Michael A Kallen, Jonathan P Troost, Barbara A De La Cruz, Alexa Bragg, Jessica Martin-Howard, Ioana Moldovan, Jennifer A Miner, Brian W Jack, Noelle E Carlozzi

“…Items with sparse responses, low-adjusted total score correlations, nonmonotonicity, low factor loading, and high residual correlations of high error modification indices were candidates for exclusion. …”
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Sample size recalculation based on the overall success rate in a randomized test-treatment trial with restricting randomization to discordant pairs by Caroline Elzner, Amra Pepić, Oke Gerke, Antonia Zapf

“…Results The empirical type I error rate is sufficiently controlled in the adaptive design as well as in the fixed design and the estimates are unbiased. …”
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Stereoacuity and ocular-associated factors in children aged 3–7 years in Guangxi, Southwest China: a cross-sectional study by Xin Xiao, Xin Xiao, Xin Xiao, Huiyao Huang, Yan Luo, Wuqiang Luo, Lili Li, Enwei Lin, Min Kong, Qi Chen

“…In the multivariate logistic regression, older age (odds ratio [OR]: 0.040–0.461 for years 4–7, 95% confidence interval [CI]: 0.018–0.613 for years 4–7, all p < 0.001) and girls (OR = 0.672, 95% CI: 0.584–0.772, p < 0.001) were protective factors, whereas interocular acuity difference [IAD] (OR = 6.906, 95% CI: 3.133–16.01, p < 0.001), mean LogMAR acuity (OR = 11.491, 95% CI: 6.065–22.153, p < 0.001), mean cylindrical error [CYLmean] (OR = 1.201, 95% CI: 1.055–1.365, p = 0.005), and anisometropia (OR = 1.452, 95% CI: 1.202–1.760, p < 0.001) were risk factors for subnormal stereoacuity.ConclusionOcular factors, including higher IAD, worse acuity, greater astigmatism, and greater anisometropia, were identified as risk factors for subnormal stereoacuity, highlighting the importance and urgency of early screening for stereoacuity and ocular risk factors in children aged 3–7 years in Guangxi.…”
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3D scanner measuring preterm infants’ head circumference and cranial volume: validation in a simulated care setting by Ronald van Gils, Ronald van Gils, Ronald van Gils, Ronald van Gils, Onno Helder, Onno Helder, René Kornelisse, Irwin Reiss, Jenny Dankelman

“…Measurement accuracy was assessed using mean or median absolute measurement error (ME), and precision by the spread of ME, represented by the 95% interval of the ME range. …”
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Safety analysis of Yttrium-90 resin microsphere selective internal radiation therapy on malignant liver tumors by Jia CAI, Shiwei TANG, Rongli LI, Mingxin KONG, Hongyan DING, Xiaofeng YUAN, Yuying HU, Ruimei LIU, Xiaoyan ZHU, Wenjun LI, Haibin ZHANG, Guanwu WANG

“…ConclusionThe adverse reactions of 90Y-SIRT for treating malignant liver tumors are mild, indicating good safety.…”
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The impact of the SKILLZ intervention on sexual and reproductive health empowerment among Zambian adolescent girls and young women: results of a cluster randomized controlled trial by Lila A. Sheira, Chama Mulubwa, Calvin Chiu, Jenala Chipungu, Chelsea Coakley, Helene Smith, Ushma D. Upadhyay, Chansa Chilambe, Besa Chibwe, Jake M. Pry, Boyd Mkandawire, Maggie Musonda, Carolyn Bolton Moore, Jenny Liu

“…Between baseline and midline, attending an intervention school was associated with a 6.21-point increase in overall score calculated using the imputed sample (standard error [SE]: 0.75, p < 0.001) compared to being in a control school (6.75% change). …”
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