Volume 43 Issue 7
Jul.  2022
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CHENG Guo, TAO Fangbiao. Research on the puberty needs to establish a multi-index evaluation and prediction system[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2022, 43(7): 961-964. doi: 10.16835/j.cnki.1000-9817.2022.07.001
Citation: CHENG Guo, TAO Fangbiao. Research on the puberty needs to establish a multi-index evaluation and prediction system[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2022, 43(7): 961-964. doi: 10.16835/j.cnki.1000-9817.2022.07.001

Research on the puberty needs to establish a multi-index evaluation and prediction system

doi: 10.16835/j.cnki.1000-9817.2022.07.001
  • Received Date: 2021-12-13
  • Rev Recd Date: 2022-04-10
  • Available Online: 2022-07-27
  • Publish Date: 2022-07-25
  • The onset and progression of puberty development is affected by interaction of genes and environments, and is linked to lifelong health. A comprehensive understanding of pubertal development in Chinese children and its influence on health outcomes becomes an important public health concern. Therefore, building an evaluation index system for Chinese children to assess the puberty onset and the temporal sequences throughout the puberty stage, and establishing a multi-dimensional predictive model for puberty trajectory lay down the foundation for this issue. This article reviews the currently available evidence referring to the assessment system and the prediction model for puberty timing, and outlines directions for future research in this area.
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  • [1]
    SMITH C E, BIRO F M. Pubertal development: what's normal/what's not [J]. Clin Obstet Gynecol, 2020, 63(3): 491-503. doi: 10.1097/GRF.0000000000000537
    [2]
    WU X, BAO L, DU Z, et al. Secular trends of age at menarche and the effect of famine exposure on age at menarche in rural Chinese women[J]. Ann Hum Biol, 2022, 49(1): 35-40. doi: 10.1080/03014460.2022.2041092
    [3]
    MENG X, LI S, DUAN W, et al. Secular trend of age at menarche in Chinese adolescents born from 1973 to 2004 [J]. Pediatrics, 2017, 140(2): e20170085. doi: 10.1542/peds.2017-0085
    [4]
    MA Q, LI R, WANG L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019[J]. Lancet Public Health, 2021, 6(12): e897-e906. doi: 10.1016/S2468-2667(21)00228-0
    [5]
    CHEN X, LIU Y, SUN X, et al. Age at menarche and risk of all-cause and cardiovascular mortality: a systematic review and dose-response Meta-analysis [J]. Menopause (New York, NY), 2018, 26(6): 670-676.
    [6]
    WALKER I V, SMITH C R, DAVIES J H, et al. Methods for determining pubertal status in research studies: literature review and opinions of experts and adolescents[J]. J Dev Orig Health Dis, 2020, 11(2): 168-187. doi: 10.1017/S2040174419000254
    [7]
    MARSHALL W A, TANNER J M. Growth and physiological development during adolescence [J]. Ann Rev Med, 1968, 19: 283-300. doi: 10.1146/annurev.me.19.020168.001435
    [8]
    CAMPISI S C, MARCHAND J D, SIDDIQUI F J, et al. Can we rely on adolescents to self-assess puberty stage? A systematic review and Meta-analysis [J]. J Clin Endocr Metab, 2020, 105(8): 2846–2856. doi: 10.1210/clinem/dgaa135
    [9]
    李丹, 史慧静, 张越, 等. 青春期性发育自评方法的有效性和适用性研究. 中华预防医学会儿少卫生分会第九届学术交流会、中国教育学会体育与卫生分会第一届学校卫生学术交流会、中国健康促进与教育协会学校分会第三届学术交流会论文集. 厦门: 中华预防医学会, 2011: 111-116.

    LI D, SHI H J, ZHANG Y, et al. The validity and applicability of self-assessment methods of adolescent sexual development. The ninth Academic Exchange Conference of the Child and Adolescent Health Section of Chinese Preventive Medicine Association, The first Academic Exchange Conference of Sports and Health Section of the Chinese Society of Education and the third Academic Exchange Conference of China Association of Health Promotion and Education. Fujian: Chinese Preventive Medicine Association, 2011: 111-116.
    [10]
    CHAVARRO J E, WATKINS D J, AFEICHE M C, et al. Validity of self-assessed sexual maturation against physician assessments and hormone levels[J]. J Pediatr, 2017, 186(3): 172-178.
    [11]
    DURDA-MASNY M, HANC T, CZAPLA Z, et al. BMI at menarche and timing of growth spurt and puberty in Polish girls-longitudinal study[J]. Anthropol Anz, 2019, 76(1): 37-47. doi: 10.1127/anthranz/2019/0920
    [12]
    BANIK S D, SALEHABADI S M, DICKINSON F. Preece-baines model 1 to estimate height and knee height growth in boys and girls from Merida, Mexico[J]. Food Nutr Bull, 2017, 38(2): 182-195. doi: 10.1177/0379572117700270
    [13]
    DEL PINO M, FANO V, ADAMO P. Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model[J]. J Pediatr Endocr Met, 2020, 33(12): 1589-1595. doi: 10.1515/jpem-2020-0298
    [14]
    ZHANG W, CHEN Z, LIU A, et al. A weighted kernel machine regression approach to environmental pollutants and infertility[J]. Stat Med, 2019, 38(5): 809-827. doi: 10.1002/sim.8003
    [15]
    GOLDBERG M, CIESIELSKI JONES A J, MCGRATH J A, et al. Urinary and salivary endocrine measurements to complement tanner staging in studies of pubertal development[J]. PLoS One, 2021, 16(5): e0251598. doi: 10.1371/journal.pone.0251598
    [16]
    黄心依. 机器学习在数据挖掘中的应用研究[J]. 信息记录材料, 2021, 22(8): 121-123.

    HUANG X Y. Research on the application of machine learning in data mining [J]. Inform Rec Mater, 2021, 22(8): 121-123.
    [17]
    JANG W Y, AHN K S, OH S, et al. Difference between bone age at the hand and elbow at the onset of puberty[J]. Medicine (Baltimore), 2022, 101(1): e28516. doi: 10.1097/MD.0000000000028516
    [18]
    TANNER J M, GIBBONS R D. Automatic bone age measurement using computerized image analysis[J]. J Pediatr Endocrinol, 1994, 7(2): 141-145.
    [19]
    SPAMPINATO C, PALAZZO S, GIORDANO D, et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Med Image Anal, 2017, 36: 41-51. doi: 10.1016/j.media.2016.10.010
    [20]
    HAO P Y, CHOKUWA S, XIE X H, et al. Skeletal bone age assessments for young children based on regression convolutional neural networks[J]. Math Biosci Eng, 2019, 16(6): 6454-6466. doi: 10.3934/mbe.2019323
    [21]
    BUI T D, LEE J J, SHIN J. Incorporated region detection and classification using deep convolutional networks for bone age assessment[J]. Artif Intell Med, 2019, 97: 1-8. doi: 10.1016/j.artmed.2019.04.005
    [22]
    LIU Y, ZHANG C, CHENG J, et al. A multi-scale data fusion framework for bone age assessment with convolutional neural networks[J]. Comput Biol Med, 2019, 108: 161-173. doi: 10.1016/j.compbiomed.2019.03.015
    [23]
    PAN L, LIU G, MAO X, et al. Development of prediction models using machine learning algorithms for girls with suspected central precocious puberty: retrospective study[J]. JMIR Med Inform, 2019, 7(1): e11728. doi: 10.2196/11728
    [24]
    WANG F, GU X, CHEN S, et al. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development[J]. Peer J, 2020, 8: e8854. doi: 10.7717/peerj.8854
    [25]
    XU Y, LIU X, PAN L, et al. Explainable dynamic multimodal variational autoencoder for the prediction of patients with suspected central precocious puberty[J]. IEEE J Biomed Health Inform, 2022, 26(3): 1362-1373. doi: 10.1109/JBHI.2021.3103271
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