[1] Valenzuela-Martínez S,Ramírez-Expósito MJ,Carrera-González MP, et al.Physiopathology of osteoporosis: nursing involvement and management[J]. Biomedicines,2023,11(4):1220. DOI:10.3390/biomedicines11041220.
[2] 章振林,夏维波,李梅,等. 原发性骨质疏松症诊疗指南(2022)[J].中华骨质疏松和骨矿盐疾病杂志,2022,15(6):573-611.DOI:10.3969/j.issn.1674-2591.2022.06.001.
[3] Crandall CJ, Ensrud KE. osteoporosis screening in younger postmenopausal women[J/OL]. JAMA,2020,323(4):367-368.DOI:10.1001/jama.2019.18343.
[4] 何海洋,杨嘉玲,雷迅.绝经后女性骨质疏松症患病率及影响因素的Meta分析[J].中国全科医学,2024,27(11):1370-1379. DOI:10.12114/j.issn.1007-9572.2023.0688.
[5] 张红霞,杨巧巧,党晨珀,等.老年性骨质疏松患者骨折风险预测工具的范围综述[J].护理学报,2023,30(3):57-62.DOI:10.16460/j.issn1008-9969.2023.03.057.
[6] Falaschi P, Marsh D.Orthogeriatrics: the management of older patients with fragility fractures[M].Cham (CH):Springer, 2021. DOI:10.1007/978-3-030-48126-1.
[7] Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane[J]. Cochrane Database Syst Rev, 2018, 10(10):ED000129. DOI:10.1002/14651858.ED000129.
[8] Moons KGM, De Groot JAH, Boumeester W, et al.Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J].PLoS Medicine,2014, 11(10):e1001744.DOI:10.1371/journal.pmed.1001744.
[9] Moons KGM,Wolff RF,Riley RD,et al.PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med,2019,170(1):W1-W33.DOI:10.7326/M18-1377.
[10] Jiang J, Liu Q, Mao Y, et al.Klotho reduces the risk of osteoporosis in postmenopausal women: a cross-sectional study of the national health and nutrition examination survey (NHANES)[J].BMC endocrine disorders, 2023, 23(1): 151. DOI:10.1186/s12902-023-01380-9.
[11] Wu X, Zhai F, Chang A, et al.Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus[J]. J Endocrinol Invest, 2023, 46(12): 2535-2546. DOI:10.1007/s40618-023-02109-0.
[12] Kwon Y, Lee J, Park JH, et al.Osteoporosis pre-screening using ensemble machine learning in postmenopausal Korean women?[J]. Healthcare (Basel),2022,10(6):1107.DOI:10.3390/healthcare10061107.
[13] Shim JG, Kim DW, Ryu KH,et al.Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women[J].Arch Osteoporos,2020,15(1):169. DOI: 10.1007/s11657-020-00802-8.
[14] Matin N, Tabatabaie O, Keshtkar A, et al.Development and validation of osteoporosis prescreening model for iranian postmenopausal women?[J]. J Diabetes Metab Disord, 2015, 14: 12. DOI:10.1186/s40200-015-0140-7.
[15] 田甜,肖静,朱宏宇,等.绝经后骨质疏松症的相关预警因素分析[J].中国妇幼保健,2024,39(11):2040-2044.DOI:10.19829/j.zgfybj.issn.1001-4411.2024.11.027.
[16] 李璠,邵晋康.绝经后2型糖尿病患者骨质疏松症影响因素分析及列线图模型构建[J].中国临床研究,2024,37(7):1039-1044.DOI:10.13429/j.cnki.cjcr.2024.07.011.
[17] 王家林,潘福敏,孔超,等.无症状老年女性骨质疏松症列线图临床预测模型的构建及效果[J]. 首都医科大学学报,2023,44(4):629-638.
[18] 茅雅倩. 基于机器学习算法构建骨质疏松风险预测模型[D].福州:福建医科大学,2022.DOI:10.27020/d.cnki.gfjyu.2022.001171.
[19] 林适,王世浩,刘树华,等.铁死亡血清学标志物在绝经后骨质疏松症发病中的作用及预测模型构建[J].中国骨质疏松杂志,2023,29(5):625-630;681.DOI:10.3969/j.issn.1006-7108.2023.05.001.
[20] 刘广锴,高毅,师伟,等.绝经后女性的生殖特点与骨质疏松症的相关性分析及个体化模型预测[J].中国骨质疏松杂志,2023,29(3):365-370;396.DOI:10.3969/j.issn.1006-7108.2023.03.010.
[21] 王秋怡,陈显英,符茂雄.2型糖尿病女性患者绝经后骨质疏松预测模型的构建与验证[J].中国现代医学杂志,2022,32(10):53-59.DOI:10.3969/j.issn.1005-8982.2022.10.010.
[22] 王馨悦. 基于X线的绝经后女性骨质疏松风险预测模型的建立与研究[D].沈阳:沈阳医学院,2023.DOI:10.27900/d.cnki.gsyyx.2022.000057.
[23] 陈健华. 绝经早期2型糖尿病女性骨质疏松预测模型研究[D].沈阳:中国医科大学, 2021.DOI:10.27652/d.cnki.gzyku.2020.000364.
[24] 王少容,梁声强,林淳峥.基于血清β-CTx、PINP及OC的列线图模型对绝经后骨质疏松预测价值的研究[J].中国妇幼健康研究,2023,34(5):53-59.DOI:10.3969/j.issn.1673-5293.2023.05.008.
[25] 陈玄,叶云金,陈娟,等.绝经后女性中医症候群骨质疏松风险预测工具构建[J].中国骨质疏松杂志,2023,29(3):356-360.DOI:10.3969/j.issn.1006-7108.2023.03.008.
[26] 吕丽,姜璐,陈诗鸿,等.210例绝经后2型糖尿病发生骨质疏松的相关因素[J].山东大学学报(医学版),2021,59(7):19-25;31.DOI: 10.6040/jissn.1671-7554.0.2021.0633.
[27] Johnston CB, Dagar M.Osteoporosis in older adults[J]. Med Clin North Am,2020,104(5):873-884. DOI:10.1016/j.mcna.2020.06.004.
[28] Xiao PL, Cui AY, Hsu CJ, et al.Global,regional prevalence, and risk factors of osteoporosis according to the world health organization diagnostic criteria: a systematic review and Meta-analysis[J]. Osteoporos International, 2022,33(10):2137-2153.DOI:10.1007/s00198-022-06454-3.
[29] Snell Kie, Levis B, Damen Jaa, et al.Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and Meta-analyses(TRIPOD-SRMA)[J].BMJ,2023,381:e073538. DOI:10.1136/bmj-2022-073538.