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Huguette S Brink Department of Endocrinology, Maasstad Hospital, Rotterdam, The Netherlands

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Aart Jan van der Lely Department of Endocrinology, Erasmus University MC, Rotterdam, The Netherlands

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Joke van der Linden Department of Endocrinology, Maasstad Hospital, Rotterdam, The Netherlands

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GD pathogenesis. Combining biomarkers and risk factors into a predictive model may add to early prediction of GD, evoke effective prevention strategies and may ultimately reduce complications associated with GD. The aim of this review is to ( 1

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Hui Long Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

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Yanhong Nie Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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Li Wang Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

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Yong Lu Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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Yan Wang Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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Yijun Cai Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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Zhen Liu Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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Miaomiao Jia Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

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Qifeng Lyu Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

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Yanping Kuang Department of Assisted Reproduction, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

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Qiang Sun Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

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FSH level ( 10 ). To date, AMH is widely used in prediction of ovarian response and clinical outcomes in humans ( 19 ) and other species, such as cow ( 20 ), sheep ( 21 ) and goats ( 22 ). However, the role of AMH in prediction of ovarian response in

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Ashley K Clift Department of Surgery and Cancer, Imperial College London, London, UK

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Omar Faiz Department of Surgery, St Mark’s Hospital, London, UK

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Robert Goldin Centre for Pathology, Imperial College London, London, UK

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John Martin Department of Gastroenterology, Imperial College London, London, UK

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Harpreet Wasan Department of Surgery and Cancer, Imperial College London, London, UK

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Marc-Olaf Liedke Department of Surgery, Westkuesten Klinikum Heide, Heide, Germany

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Erik Schloericke Department of Surgery, Westkuesten Klinikum Heide, Heide, Germany

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Anna Malczewska Department of Surgery and Cancer, Imperial College London, London, UK
Department of Pathophysiology and Endocrinology, Medical University of Silesia, Katowice, Poland

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Guido Rindi Institute of Anatomic Pathology, Universita Cattolica del Sacro Cuore, Rome, Italy

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Mark Kidd Wren Laboratories, Branford, Connecticut, USA

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Irvin M Modlin Emeritus Professor Gastrointestinal Surgery, School of Medicine, Yale University, New Haven, Connecticut, USA

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Andrea Frilling Department of Surgery and Cancer, Imperial College London, London, UK

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represent s.e.m. Data for all patients are included. The data marks in each plot from left to right represent high-, medium- and low-risk groups, respectively. Discussion NET patient prognosis prediction is limited by the paucity of

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Nidan Qiao Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

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). Machine learning may help to build a more reliable aided diagnostic tool for neuroradiologists and neuropathologists. Better prediction of clinical outcomes in these patients may provide better clinical decision support for either neuroendocrinologists or

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Thomas Reinehr Pediatric Endocrinology, Diabetes and Nutrition Medicine, Vestische Children’s Hospital, University of Witten/Herdecke, Datteln, Germany

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Martin Carlsson Endocrine Care, Pfizer Inc, New York, New York, USA

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Dionisios Chrysis Division of Pediatric Endocrinology, University of Patras, Patras, Greece

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Cecilia Camacho-Hübner Endocrine Care, Pfizer Inc, New York, New York, USA

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Introduction Prediction of adult height is a frequently requested procedure in pediatric endocrinology. The commonly used methods for adult height prediction are bone age determination of the wrist and fingers of the left hand by Greulich

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Tomaž Kocjan Department of Endocrinology, Diabetes and Metabolic Diseases, University Medical Centre Ljubljana, Ljubljana, Slovenia
Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia

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Gaj Vidmar Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
University Rehabilitation Institute, Ljubljana, Slovenia
FAMNIT, University of Primorska, Koper, Slovenia

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Peter Popović Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
Clinical Institute of Radiology, University Medical Centre Ljubljana, Ljubljana, Slovenia

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Milenko Stanković Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
Clinical Institute of Radiology, University Medical Centre Ljubljana, Ljubljana, Slovenia

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prediction tools based on patient clinical and biochemical characteristics, which are obtained during the routine diagnostic work-up, might be employed to better select patients for AVS ( 13 ). These aids mainly rely on the well-known observation that LPA is

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Marieke S Velema Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Evie J M Linssen Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Ad R M M Hermus Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Hans J M M Groenewoud Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Gert-Jan van der Wilt Department of Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands

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Antonius E van Herwaarden Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Jacques W M Lenders Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
Department of Internal Medicine III, University Hospital Carl Gustav Carus at the TU Dresden, Dresden, Germany

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Henri J L M Timmers Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

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Jaap Deinum Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
Department of Internal Medicine III, University Hospital Carl Gustav Carus at the TU Dresden, Dresden, Germany

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development and validation of prediction models ( 5 ). Patients Our cohort consisted of adult patients who underwent an SLT for clinically suspected PA at the Radboud Adrenal Center, a tertiary center of expertise for patients with adrenal disorders in

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Olav Inge Håskjold Department of Breast and Endocrine Surgery, University Hospital of North Norway, Tromsø, Norway

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Henrik Stenestø Foshaug UiT – The Arctic University of Norway, Institute of Clinical Medicine

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Therese Benedikte Iversen Department of Radiology, University Hospital of North Norway, Harstad, Norway

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Helga Charlotte Kjøren Department of Radiology, University Hospital of North Norway, Harstad, Norway

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Vegard Heimly Brun Department of Breast and Endocrine Surgery, University Hospital of North Norway, Tromsø, Norway
UiT – The Arctic University of Norway, Institute of Clinical Medicine

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carcinomas. All three anaplastic cancers were correctly identified. We saw no medullary thyroid cancers in our study period. Figure 2 Confusion matrix comparing ultrasound prediction with actual histopathological diagnosis. Prediction was based on non

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Tianze Ding T Ding, Department of Clinical Nutrition, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Peijie Liu P Liu, Department of Clinical Nutrition, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Jie Jia J Jia, Department of Clinical Nutrition, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Hui Wu H Wu, Department of Nutrition, Seventh People’s Hospital of shanghai University of Traditional Chinese Medicine, Shanghai, China

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Jie Zhu J Zhu, Nutrition and Foods Program, School of Family and Consumer Sciences, Texas State University, San Marcos, United States

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Kefeng Yang K Yang, Department of Clinical Nutrition, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Introduction: Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects.

Methods: A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data was investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using Logistic Regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by Accuracy, Sensitivity, Specificity, Area Under Curve (AUC) and Hosmer-Lemeshow test.

Results: Blood glucose, age, pre-pregnancy body mass index (BMI), triglycerides and high-density lipoprotein cholesterol (HDL) were the top five features according to the feature selection. Among the three algorithms, XGBoost performed best with an AUC of 0.788, LightGBM came second (AUC = 0.749), and Logistic Regression performed the worst (AUC = 0.712). In addition, XGBoost and LightGBM both achieved a fairly good performance when dietary information was included, surpassing their performance on the non-dietary dataset (0.788 vs. 0.718 in XGBoost; 0.749 vs. 0.726 in LightGBM).

Conclusion: XGBoost and LightGBM algorithms outperform Logistic Regression in predicting GDM among the Chinese pregnant women. In addition, dietary data may have a positive effect on improving model performance, which deserves more in-depth investigation with larger sample size.

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Yang Lv Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Xu Han Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Chunyan Zhang Department of Clinical Laboratory, Zhongshan Hospital, Fudan University, Shanghai, China

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Yuan Fang Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Ning Pu Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Yuan Ji Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China

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Dansong Wang Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Xu Xuefeng Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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Wenhui Lou Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

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surgical/needled specimen can give rise to the prognosis prediction and nonoperative management guidance to some extents, but this is usually based on a fixed small biopsy sample that may not reflect the heterogeneity present in advanced tumors ( 2

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