{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:51:13Z","timestamp":1781520673157,"version":"3.54.1"},"reference-count":43,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Ministry of Trade Industry & Energ","award":["K_G012001185601"],"award-info":[{"award-number":["K_G012001185601"]}]},{"name":"GRRC program of Gyeonggi Province","award":["GRRC-Gachon2023(B01)"],"award-info":[{"award-number":["GRRC-Gachon2023(B01)"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Exacerbation of chronic respiratory diseases leads to poor prognosis and a significant socioeconomic burden. To address this issue, an artificial intelligence model must assess patient prognosis early and classify patients into high- and low-risk groups. This study aimed to develop a model to predict in-hospital mortality in patients with chronic respiratory disease using demographic, clinical, and environmental factors, specifically air pollution exposure levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study included 6272 patients diagnosed with chronic respiratory diseases comprising 39 risk factors. Air pollution indicators such as particulate matter (PM10), fine particulate matter (PM2.5), CO, NO\n                      <jats:sub>2<\/jats:sub>\n                      , O\n                      <jats:sub>3<\/jats:sub>\n                      , and SO\n                      <jats:sub>2<\/jats:sub>\n                      were used based on long-term and short-term exposure levels. Logistic regression, support vector machine, random forest, and extreme gradient boost were used to develop prediction models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AUCs for the four models were 0.932, 0.935, 0.933, and 0.944. The key risk factors that significantly influenced predictions included blood urea nitrogen, red blood cell distribution width, respiratory rate, and age, which were positively correlated with mortality prediction. In contrast, albumin, lymphocyte count, diastolic blood pressure, and SpO2 were negatively correlated with mortality prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed a prediction model for in-hospital mortality in patients with chronic respiratory disease and demonstrated a relatively high predictive performance. By incorporating environmental factors, such as air pollution exposure levels, the model with the best performance suggested that 365 days of exposure to air pollution was a key risk factor in mortality prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251326703","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T07:35:35Z","timestamp":1743752135000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations"],"prefix":"10.1177","volume":"11","author":[{"given":"Seung Yeob","family":"Ryu","sequence":"first","affiliation":[{"name":"Department of Biohealth &amp; Medical Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Korea"},{"name":"Medical Devices R&amp;D Center, Gachon University Gil Medical Center, Incheon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8694-2169","authenticated-orcid":false,"given":"Seon Min","family":"Lee","sequence":"additional","affiliation":[{"name":"Medical Devices R&amp;D Center, Gachon University Gil Medical Center, Incheon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-0051","authenticated-orcid":false,"given":"Young Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"Gachon Biomedical Convergence Institute, Gachon University Gil Medical Center, Incheon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Medicine, Gachon University Gil Medical Center, Incheon, Korea"},{"name":"Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam-si, Gyeonggi-do, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1186\/s12931-023-02628-7","article-title":"Deep learning prediction of hospital readmissions for asthma and COPD","volume":"24","author":"Lopez K","year":"2023","unstructured":"Lopez K, Li H, Lipkin-Moore Z, et al. Deep learning prediction of hospital readmissions for asthma and COPD. Respir Res 2023; 24: 311. 20231213.","journal-title":"Respir Res"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.2147\/COPD.S234942"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1111\/crj.13606","article-title":"A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease","volume":"17","author":"Ji Z","year":"2023","unstructured":"Ji Z, Li X, Lei S, et al. A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease. Clin Respir J 2023; 17: 707\u2013718. 20230321.","journal-title":"Clin Respir J"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1007\/s12325-023-02743-3","article-title":"Machine learning approaches to predict asthma exacerbations: a narrative review","volume":"41","author":"Molfino NA","year":"2024","unstructured":"Molfino NA, Turcatel G, Riskin D. Machine learning approaches to predict asthma exacerbations: a narrative review. Adv Ther 2024; 41: 534\u2013552. 20231219.","journal-title":"Adv Ther"},{"key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2147\/COPD.S147796","article-title":"Examining 30-day COPD readmissions through the emergency department","volume":"13","author":"Rezaee ME","year":"2018","unstructured":"Rezaee ME, Ward CE, Nuanez B, et al. Examining 30-day COPD readmissions through the emergency department. Int J Chron Obstruct Pulmon Dis 2018; 13: 109\u2013120. 20171227.","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12931-020-01482-1"},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.2147\/COPD.S435921","article-title":"Predictive role of white blood cell differential count for the development of acute exacerbation in Korean chronic obstructive pulmonary disease","volume":"19","author":"Yoon EC","year":"2024","unstructured":"Yoon EC, Koo SM, Park HY, et al. Predictive role of white blood cell differential count for the development of acute exacerbation in Korean chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2024; 19: 17\u201331. 20240104.","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.ajem.2018.06.062","article-title":"Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED","volume":"36","author":"Goto T","year":"2018","unstructured":"Goto T, Camargo CAJr., Faridi MK, et al. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med 2018; 36: 1650\u20131654. 20180628.","journal-title":"Am J Emerg Med"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","first-page":"20220124","DOI":"10.1183\/23120541.00452-2021","article-title":"Predicting in-hospital death in pneumonic COPD exacerbation via BAP-65, CURB-65 and machine learning","volume":"8","author":"Shiroshita A","year":"2022","unstructured":"Shiroshita A, Kimura Y, Shiba H, et al. Predicting in-hospital death in pneumonic COPD exacerbation via BAP-65, CURB-65 and machine learning. ERJ Open Res 2022; 8: 20220124.","journal-title":"ERJ Open Res"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.12.051"},{"key":"e_1_3_3_12_2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1186\/s12890-023-02570-w","article-title":"Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis","volume":"23","author":"Xiong S","year":"2023","unstructured":"Xiong S, Chen W, Jia X, et al. Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis. BMC Pulm Med 2023; 23: 278. 20230728.","journal-title":"BMC Pulm Med"},{"key":"e_1_3_3_13_2","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1016\/j.jaci.2019.02.018","article-title":"Impact of weather and climate change with indoor and outdoor air quality in asthma: a work group report of the AAAAI environmental exposure and respiratory health committee","volume":"143","author":"Poole JA","year":"2019","unstructured":"Poole JA, Barnes CS, Demain JG, et al. Impact of weather and climate change with indoor and outdoor air quality in asthma: a work group report of the AAAAI environmental exposure and respiratory health committee. J Allergy Clin Immunol 2019; 143: 1702\u20131710. 20190228.","journal-title":"J Allergy Clin Immunol"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"20200827","DOI":"10.3390\/ijerph17176212","article-title":"Impact of air pollution on asthma outcomes","volume":"17","author":"Tiotiu AI","year":"2020","unstructured":"Tiotiu AI, Novakova P, Nedeva D, et al. Impact of air pollution on asthma outcomes. Int J Environ Res Public Health 2020; 17: 20200827.","journal-title":"Int J Environ Res Public Health"},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","first-page":"23313","DOI":"10.1038\/s41598-021-02395-9","article-title":"Thirty-day hospital readmission prediction model based on common data model with weather and air quality data","volume":"11","author":"Ryu B","year":"2021","unstructured":"Ryu B, Yoo S, Kim S, et al. Thirty-day hospital readmission prediction model based on common data model with weather and air quality data. Sci Rep 2021; 11: 23313. 20211202.","journal-title":"Sci Rep"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1093\/eurheartj\/ehy451","article-title":"Blood pressure, heart rate, and mortality in chronic obstructive pulmonary disease: the SUMMIT trial","volume":"39","author":"Byrd JB","year":"2018","unstructured":"Byrd JB, Newby DE, Anderson JA, et al. Blood pressure, heart rate, and mortality in chronic obstructive pulmonary disease: the SUMMIT trial. Eur Heart J 2018; 39: 3128\u20133134.","journal-title":"Eur Heart J"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s10877-019-00415-8","article-title":"Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring","volume":"34","author":"Elvekjaer M","year":"2020","unstructured":"Elvekjaer M, Aasvang EK, Olsen RM, et al. Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring. J Clin Monit Comput 2020; 34: 1051\u20131060. 20191111.","journal-title":"J Clin Monit Comput"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/1440-1681.12357"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1373\/clinchem.2016.267450","article-title":"Association of blood eosinophil and blood neutrophil counts with asthma exacerbations in the Copenhagen general population study","volume":"63","author":"Vedel-Krogh S","year":"2017","unstructured":"Vedel-Krogh S, Fallgaard Nielsen S, Lange P, et al. Association of blood eosinophil and blood neutrophil counts with asthma exacerbations in the Copenhagen general population study. Clin Chem 2017; 63: 823\u2013832. 20170216.","journal-title":"Clin Chem"},{"key":"e_1_3_3_20_2","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1159\/000515180","article-title":"Low-blood lymphocyte number and lymphocyte decline as key factors in COPD outcomes: a longitudinal cohort study","volume":"100","author":"Semenzato U","year":"2021","unstructured":"Semenzato U, Biondini D, Bazzan E, et al. Low-blood lymphocyte number and lymphocyte decline as key factors in COPD outcomes: a longitudinal cohort study. Respiration 2021; 100: 618\u2013630. 20210426.","journal-title":"Respiration"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.2217\/bmm.15.112"},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","DOI":"10.1111\/ijcp.14730","article-title":"Can red blood cell distribution width (RDW) level predict the severity of acute exacerbation of chronic obstructive pulmonary disease (AECOPD)?","volume":"75","author":"Alparslan Bekir S","year":"2021","unstructured":"Alparslan Bekir S, Tuncay E, Gungor S, et al. Can red blood cell distribution width (RDW) level predict the severity of acute exacerbation of chronic obstructive pulmonary disease (AECOPD)? Int J Clin Pract 2021; 75: e14730. 20210819.","journal-title":"Int J Clin Pract"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.3109\/15412555.2012.758697"},{"key":"e_1_3_3_24_2","first-page":"196","article-title":"Could \u201cred cell distribution width\u201d predict COPD severity?","volume":"22","author":"Tertemiz KC","year":"2016","unstructured":"Tertemiz KC, Ozgen Alpaydin A, Sevinc C, et al. Could \u201cred cell distribution width\u201d predict COPD severity? Rev Port Pneumol (2006) 2016; 22: 196\u2013201. 20160122.","journal-title":"Rev Port Pneumol (2006)"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2016.05.003"},{"key":"e_1_3_3_26_2","first-page":"20230803","article-title":"Association of Serum calcium with the risk of chronic obstructive pulmonary disease: a prospective study from UK biobank","volume":"15","author":"Wan X","year":"2023","unstructured":"Wan X, Chen L, Zhu Z, et al. Association of Serum calcium with the risk of chronic obstructive pulmonary disease: a prospective study from UK biobank. Nutrients 2023; 15: 20230803.","journal-title":"Nutrients"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1177\/14799731211060051"},{"key":"e_1_3_3_28_2","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.2147\/COPD.S412106","article-title":"Elevated BUN upon admission as a predictor of in-hospital mortality among patients with acute exacerbation of COPD: a secondary analysis of multicenter cohort study","volume":"18","author":"Zhang J","year":"2023","unstructured":"Zhang J, Qin Y, Zhou C, et al. Elevated BUN upon admission as a predictor of in-hospital mortality among patients with acute exacerbation of COPD: a secondary analysis of multicenter cohort study. Int J Chron Obstruct Pulmon Dis 2023; 18: 1445\u20131455. 20230713.","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"e_1_3_3_29_2","doi-asserted-by":"crossref","first-page":"20210113","DOI":"10.3390\/jcm10020269","article-title":"Serum albumin concentrations in stable chronic obstructive pulmonary disease: a systematic review and meta-analysis","volume":"10","author":"Zinellu E","year":"2021","unstructured":"Zinellu E, Fois AG, Sotgiu E, et al. Serum albumin concentrations in stable chronic obstructive pulmonary disease: a systematic review and meta-analysis. J Clin Med 2021; 10: 20210113.","journal-title":"J Clin Med"},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","first-page":"1109910","DOI":"10.3389\/fmed.2023.1109910","article-title":"Relationship between human serum albumin and in-hospital mortality in critical care patients with chronic obstructive pulmonary disease","volume":"10","author":"Ling M","year":"2023","unstructured":"Ling M, Huiyin L, Shanglin C, et al. Relationship between human serum albumin and in-hospital mortality in critical care patients with chronic obstructive pulmonary disease. Front Med (Lausanne) 2023; 10: 1109910. 20230427.","journal-title":"Front Med (Lausanne)"},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.jaip.2016.11.007","article-title":"Identifying risk of future asthma attacks using UK medical record data: a respiratory effectiveness group initiative","volume":"5","author":"Blakey JD","year":"2017","unstructured":"Blakey JD, Price DB, Pizzichini E, et al. Identifying risk of future asthma attacks using UK medical record data: a respiratory effectiveness group initiative. J Allergy Clin Immunol Pract 2017; 5: 1015\u20131024 e1018. 20161222.","journal-title":"J Allergy Clin Immunol Pract"},{"key":"e_1_3_3_32_2","volume-title":"Support vector machines","author":"Steinwart I","year":"2008","unstructured":"Steinwart I, Christmann A. Support vector machines. 1st ed. New York: Springer, 2008, p.xvi, 601 p.","edition":"1"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_3_35_2","first-page":"35","volume-title":"Ensembles of bagged TAO trees consistently improve over random forests, AdaBoost and gradient boostingProceedings of the 2020 ACM-IMS on Foundations of Data Science Conference","author":"Carreira-Perpi\u00f1\u00e1n M\u00c1","year":"2020","unstructured":"Carreira-Perpi\u00f1\u00e1n M\u00c1, Zharmagambetov A. Ensembles of bagged TAO trees consistently improve over random forests, AdaBoost and gradient boosting. Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference. Virtual Event, USA: Association for Computing Machinery, 2020, p. 35\u201346."},{"key":"e_1_3_3_36_2","volume-title":"Hands-On Gradient Boosting with XGBoost and Scikit-learn: Perform Accessible Machine Learning and Extreme Gradient Boosting with Python","author":"Wade C","year":"2020","unstructured":"Wade C, Glynn K. Hands-On Gradient Boosting with XGBoost and Scikit-learn: Perform Accessible Machine Learning and Extreme Gradient Boosting with Python. 1st ed. Birmingham: Packt Publishing, Limited, 2020.","edition":"1"},{"key":"e_1_3_3_37_2","first-page":"1","article-title":"All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously","volume":"20","author":"Fisher A","year":"2019","unstructured":"Fisher A, Rudin C, Dominici F. All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously. J Mach Learn Res 2019; 20: 1\u201318.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103502"},{"key":"e_1_3_3_39_2","first-page":"4768","volume-title":"A unified approach to interpreting model predictionsProceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg SM","year":"2017","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc., 2017, p. 4768\u20134777."},{"key":"e_1_3_3_40_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.12746\/swrccc.v5i19.391","article-title":"The receiver operating characteristic (ROC) curve","volume":"5","author":"Shengping Y","year":"2017","unstructured":"Shengping Y, Gilbert B. The receiver operating characteristic (ROC) curve. The Southwest Respiratory Crit Care Chronicles 2017; 5: 34\u201336.","journal-title":"The Southwest Respiratory Crit Care Chronicles"},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1165\/rcmb.2017-0373OC","article-title":"Systemic markers of adaptive and innate immunity are associated with chronic obstructive pulmonary disease severity and spirometric disease progression","volume":"58","author":"Halper-Stromberg E","year":"2018","unstructured":"Halper-Stromberg E, Yun JH, Parker MM, et al. Systemic markers of adaptive and innate immunity are associated with chronic obstructive pulmonary disease severity and spirometric disease progression. Am J Respir Cell Mol Biol 2018; 58: 500\u2013509.","journal-title":"Am J Respir Cell Mol Biol"},{"key":"e_1_3_3_42_2","first-page":"941","article-title":"Low diastolic blood pressure and adverse outcomes in inpatients with acute exacerbation of chronic obstructive pulmonary disease: a multicenter cohort study","volume":"136","author":"Zhou C","year":"2023","unstructured":"Zhou C, Yi Q, Luo Y, et al. Low diastolic blood pressure and adverse outcomes in inpatients with acute exacerbation of chronic obstructive pulmonary disease: a multicenter cohort study. Chin Med J (Engl) 2023; 136: 941\u2013950. 20030407.","journal-title":"Chin Med J (Engl)"},{"key":"e_1_3_3_43_2","first-page":"20211012","article-title":"The value of a complete blood count (CBC) for sepsis diagnosis and prognosis","volume":"11","author":"Agnello L","year":"2021","unstructured":"Agnello L, Giglio RV, Bivona G, et al. The value of a complete blood count (CBC) for sepsis diagnosis and prognosis. Diagnostics (Basel) 2021; 11: 20211012.","journal-title":"Diagnostics (Basel)"},{"key":"e_1_3_3_44_2","doi-asserted-by":"crossref","first-page":"104879","DOI":"10.1016\/j.envint.2019.05.073","article-title":"Long-term exposure to PM(2.5) and ozone and hospital admissions of medicare participants in the southeast USA","volume":"130","author":"Danesh Yazdi M","year":"2019","unstructured":"Danesh Yazdi M, Wang Y, Di Q, et al. Long-term exposure to PM(2.5) and ozone and hospital admissions of medicare participants in the southeast USA. Environ Int 2019; 130: 104879. 20190622.","journal-title":"Environ Int"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251326703","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076251326703","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251326703","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:51:34Z","timestamp":1777398694000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076251326703"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":43,"alternative-id":["10.1177\/20552076251326703"],"URL":"https:\/\/doi.org\/10.1177\/20552076251326703","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]},"article-number":"20552076251326703"}}