The contributions of brain structural and functional variance in predicting age, sex and treatment by Ning-Xuan Chen, Gui Fu, Xiao Chen, Le Li, Michael P. Milham, Su Lui, Chao-Gan Yan

“…We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. …”
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Assessing the performance of the Iceland screens, treats, or prevents multiple myeloma (iStopMM) model in a multicultural Bronx cohort: implications for monoclonal gammopathy of un... by Rajvi Gor, Jeevan Shivakumar, Pallavi Surana, John Wei, Irina Murakhovskaya, Mendel Goldfinger, Noah Kornblum, Lauren Shapiro, Aditi Shastri, Ridhi Gupta, David Levitz, Marina Konopleva, Eric Feldman, Kira Gritsman, R. Alejandro Sica, Ioannis Mantzaris, Amit Verma, Dennis Cooper, Murali Janakiram, Nishi Shah

“…The area under the receiver operating characteristic (AUROC) curve assessed the iStopMM model’s performance in predicting ≥10% plasma cells, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. …”
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AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer’s disease: a multicenter study by Simona Aresta, Raffaello Nemni, Moreno Zanardo, Graziella Sirabian, Dario Capelli, Marco Alì, Paolo Vitali, Paolo Vitali, Enrico Giuseppe Bertoldo, Valentina Fiolo, Lilla Bonanno, Giuseppa Maresca, Petronilla Battista, Francesco Sardanelli, Francesco Sardanelli, Francesca Benedetta Pizzini, Isabella Castiglioni, Christian Salvatore, Christian Salvatore

“…For the three-class classification of causal hypotheses (n = 112), the AI performance vs. biomarker-based diagnosis was: positive predictive value 91% [95% CI: 84–96%]; negative predictive value 100%, and accuracy 91% [84–96%]. …”
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Diagnostic Efficacy of Cervical Elastography in Predicting Spontaneous Preterm Birth in Pregnancies with Threatened Preterm Labor by Hayan Kwon, Ji-Hee Sung, Hyun Soo Park, Ja-Young Kwon, Yun Ji Jung, Hyun-Joo Seol, Hyun Mi Kim, Won Joon Seong, Han Sung Hwang, Soo-Young Oh, on behalf of The Korean Consortium for the Study of Cervical Elastography in Prediction of Preterm Delivery

“…This study aimed to determine the predictive value of cervical elastography for preterm delivery before 37 weeks of gestation in patients with threatened PTL and a cervical length greater than 15 mm. …”
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Early Neonatal Mortality (< 24 h) in Ecuador: A Population-Based Study on the Impact of Apgar Score, Gestational Age, Birth Weight, Delivery Type, and Healthcare Level by Iván Dueñas-Espín, María Alejandra Montaluisa, Andrea Aguilar-Molina, Fernando Aguinaga, Luciana Armijos-Acurio, Ruth Jimbo-Sotomayor, Ángela León Cáceres, María F. Rivadeneira, Silvana Rivera-Guerra, Xavier Sánchez, Betzabé Tello

“…These findings underscore the importance of Apgar evaluation in neonates and suggest a predictive value of the score for early neonatal mortality.…”
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Evaluating the diagnostic accuracy of 99mTc-labeled somatostatin receptor imaging for suspected pheochromocytomas and paragangliomas by Bo Li, Xintao Ding, Jie Zhang, Minmin Tang, Huimin Liu, Xinyu Wu, Yongju Gao

“…The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of the imaging modality were calculated. …”
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The diagnostic utility of lung ultrasound in the assessment of interstitial lung disease associated with rheumatoid arthritis by Shaoyu Zheng, Zexuan Zhou, Guangzhou Du, Qingzi Chen, Shaoqi Chen, Jianqun Lin, Shijian Hu, Weijin Zhang, Kedi Zheng, Jinghua Zhuang, Meigan Huang, Barbara Ruaro, Cosimo Bruni, Anna-Maria Hoffmann-Vold, Marco Matucci-Cerinic, Daniel E. Furst, Yukai Wang

“…The diagnostic sensitivity and specificity of LUS were 86.84% and 77.27%, respectively. The positive predictive value, negative predictive value, a positive likelihood ratio and a negative likelihood ratio were 86.84%, 77.27%, 3.82, and 0.17, respectively. …”
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Predicting the Risk of Preterm Birth Throughout Pregnancy Based on a Novel Transcriptomic Signature by Yang Pan, Yuxin Ran, Dongni Huang, Nanlin Yin, Yanqing Wen, Yan Jiang, Yamin Liu, Hongbo Qi

“…It aimed to identify the transcriptomic signature essential for the occurrence of PTB and evaluate its predictive value in early, mid, and late pregnancy and in women with threatened preterm labor (TPTL). …”
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Diagnostic Effectiveness of Transforming Growth Factor Beta 1 in Assessing the Risk of Developing Graft Dysfunction in Liver Recipient Children by R. M. Kurabekova, O. M. Tsiroulnikova, I. E. Pashkova, A. G. Olefirenko, O. E. Gichkun, L. V. Makarova, R. A. Monakhov, P. O. Shevchenko

“…The accuracy of the method, the positive predictive value and the negative predictive value of the results were 78, 83 and 77 %, respectively. …”
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Development and validation of a clinical-friendly model for predicting 180-day mortality in the older with community-acquired pneumonia by Bingxuan Weng, Lixue Huang, Wenshu Jiao, Yuanqi Wang, Mengyuan Wang, Xuefeng Zhong, Xunliang Tong, Jin Jin, Yanming Li

“…The performance of model in predicting both short- and long-term mortality was compared with CURB-65 using AUC, sensitivity, specificity, negative predictive value and positive predictive value. Results A total of 619 patients, with a median age of 78 years (IQR: 70.5–85.0), were included in the analysis. …”
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Serum sFlt-1/PlGF ratio combined with ANXA2 correlates with adverse pregnancy outcomes in women with suspected preeclampsia by YaNuan Chen, LiYing Huang, JingWen Zhang, Ning Guo

“…The combination of the two indicators had a high predictive value for adverse pregnancy outcomes in women with suspected PE (AUC 0.858, 95CI% 0.785–0.931).Conclusion An sFlt-1/PlGF ratio ≥ 85 combined with circulating ANXA2 < 6.13 ng/mL accurately identifies those at high risk for PE-related adverse outcomes, and its early warning efficacy is significantly superior to current single-biomarker strategies. …”
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Application of continuous renal replacement therapy (CRRT) in patients with severe acute pancreatitis: an analytical study by Yiru Weng, Zhouzhou Dong, Gongjie Ye, Linhui Shi, Panpan Liu, Tingting Zhou, Honghua Ye

“…Using logistic regression, we identified CRRT failure predictors, evaluated their predictive value via ROC analysis, and developed/validated a nomogram prediction model. …”
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Avoidant restrictive food intake disorder (ARFID) in Swedish preschool children: a screening study by Lisa Dinkler, Katarzyna Brimo, Helena Holmäng, Kahoko Yasumitsu-Lovell, Ralf Kuja-Halkola, Anne-Katrin Kantzer, Zerina Omanovic, Narufumi Suganuma, Masamitsu Eitoku, Mikiya Fujieda, Elisabeth Fernell, Per Möllborg, Rachel Bryant-Waugh, Christopher Gillberg, Maria Råstam

“…Results Of the 42 children (6.5%) who screened positive for ARFID, 29 were followed up via diagnostic interview, and 21 received an ARFID diagnosis, yielding a positive predictive value of 72%. Negative predictive value, sensitivity, specificity, and overall accuracy of the ARFID-Brief Screener were 94%, 91%, 79%, and 84%, respectively. …”
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Diagnostic performance of ultrasound S-Detect technology in evaluating BI-RADS-4 breast nodules ≤ 20 mm and > 20 mm by Boyuan Xing, Chen Gu, Chenghui Fu, Bingyi Zhang, Yandi Tan

“…Using pathology results as the gold standard, we applied receiver operator characteristics (ROC), sensitivity (SE), specificity (SP), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) to analyze the diagnostic value of BI-RADS, S-Detect, and the two techniques in combination (Co-Detect) in the diagnosis of BI-RADS 4 breast nodules ≤ 20 mm and > 20 mm. …”
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Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study by Jingchao Lei, Jia Zhai, Yao Zhang, Jing Qi, Chuanzheng Sun

“…Model performance was evaluated using accuracy, balanced accuracy, Brier score, detection prevalence, F1-score, Jaccard index, κ coefficient, Matthews correlation coefficient, negative predictive value, positive predictive value, precision, recall, area under the receiver operating characteristic curve (ROC-AUC), precision-recall AUC, and decision curve analysis. …”
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Don’t go chasing waterfalls: Multiple factor prediction of injuries in a performance context by Melanie I. Stuckey, Dean Kriellaars

“…Injury surveillance informs risk management strategies for athletes and performing artists yet the predictive value of multiple factors is understudied. This study analyzed demographic, sleep, fatigue, body composition, symmetry/proportionality, and psychological data from a cohort of circus arts students to predict injury presence and duration using regression models. …”
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The quantitative prediction of tectonic fractures of Longmaxi formation in Dingshan area, southeastern Sichuan Basin by Jiatong Xie, Qirong Qin, Cunhui Fan, Dongfeng Hu, Hu Li

“…Compared the fracture density predictive value and the observed value of 15 wells, all wells relative error less than 25%. …”
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Does the Decrease in Neutrophil-lymphocyte Ratio after BCG Treatment Be a Prognostic Marker for NMIBC? by Aykut Başer

“…We aimed to determine the predictive value of the neutrophil-lymphocyte ratio (NLR) for recurrence and/or progression of the disease.Materials and Methods:Seventy patients with high-risk NMIBC according to the European Organization for Research and Treatment of Cancer (EORTC) risk classification who were receiving Bacillus Calmette-Guerin (BCG) treatment and were followed up at our clinic were included in the study.Results:The average score level and NLR values differed significantly on patient classification according to the EORTC Progression and Recurrence Risk score. …”
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Scoring Systems to Predict Survival or Neurological Recovery after Out-of-hospital Cardiac Arrest by Rishi Naik, Indrajeet Mandal, Diana A Gorog

“…NULL-PLEASE and CAST are recommended as the best scores to predict mortality and neurological outcome, respectively, due to the extent of external validation, ease of use and high predictive value of the variables. Whether use of these scores can lead to more cost-effective service delivery remains unclear.…”
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Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks by Nightingale Health Biobank Collaborative Group

“…Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.…”
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