{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T18:02:32Z","timestamp":1793815352701,"version":"4.6.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003075","name":"Chongqing Municipal Public Health Bureau, Chongqing People's Municipal Government","doi-asserted-by":"publisher","award":["2021MSXM052"],"award-info":[{"award-number":["2021MSXM052"]}],"id":[{"id":"10.13039\/501100003075","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010037","name":"Chongqing Youth Science and Technology Talent Training Project","doi-asserted-by":"publisher","award":["cstc2019jscx-msxmX0104"],"award-info":[{"award-number":["cstc2019jscx-msxmX0104"]}],"id":[{"id":"10.13039\/501100010037","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012458","name":"Chongqing Basic Science and Advanced Technology Research Program","doi-asserted-by":"crossref","award":["cstc2018jszx-cyztzxX0017"],"award-info":[{"award-number":["cstc2018jszx-cyztzxX0017"]}],"id":[{"id":"10.13039\/501100012458","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019031","name":"Chongqing Municipality Key Research and Development Program of China","doi-asserted-by":"publisher","award":["CSTC2021jscx-gksb-N0025"],"award-info":[{"award-number":["CSTC2021jscx-gksb-N0025"]}],"id":[{"id":"10.13039\/501100019031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-023-09833-4","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T02:03:07Z","timestamp":1688090587000},"page":"8869-8878","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography"],"prefix":"10.1007","volume":"33","author":[{"given":"Fang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqin","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5149-2496","authenticated-orcid":false,"given":"Chen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"9833_CR1","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.3855\/jidc.11265","volume":"13","author":"W Yang","year":"2019","unstructured":"Yang W, Ma Y, Wang J, Li Y, Zhang Y, Zhang T (2019) Fungal pneumonia manifesting as cavitary lesions in a critically ill elderly patient. J Infect Dev Ctries 13:1170\u20131173","journal-title":"J Infect Dev Ctries"},{"key":"9833_CR2","doi-asserted-by":"crossref","unstructured":"Cilloniz C, Martin-Loeches I, Garcia-Vidal C, San Jose A, Torres A (2016) Microbial etiology of pneumonia: epidemiology, diagnosis and resistance patterns.  Int J Mol Sci 17(12):2120","DOI":"10.3390\/ijms17122120"},{"key":"9833_CR3","doi-asserted-by":"publisher","first-page":"266","DOI":"10.5582\/bst.2021.01342","volume":"15","author":"L Shen","year":"2021","unstructured":"Shen L, Wang L, Liu C, Shi S, Takahashi T, Wang T (2021) Community-acquired pneumonia: trends in and research on drug resistance and advances in new antibiotics. Biosci Trends 15:266\u2013275","journal-title":"Biosci Trends"},{"key":"9833_CR4","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1186\/s12967-020-02324-w","volume":"18","author":"X Li","year":"2020","unstructured":"Li X, Zeng W, Li X et al (2020) CT imaging changes of corona virus disease 2019(COVID-19): a multi-center study in Southwest China. J Transl Med 18:154","journal-title":"J Transl Med"},{"key":"9833_CR5","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1183\/09031936.00111012","volume":"42","author":"SF van Vugt","year":"2013","unstructured":"van Vugt SF, Verheij TJ, de Jong PA et al (2013) Diagnosing pneumonia in patients with acute cough: clinical judgment compared to chest radiography. Eur Respir J 42:1076\u20131082","journal-title":"Eur Respir J"},{"key":"9833_CR6","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1007\/s00330-019-06573-2","volume":"30","author":"D Albano","year":"2020","unstructured":"Albano D, Messina C, Vitale J, Sconfienza LM (2020) Imaging of sarcopenia: old evidence and new insights. Eur Radiol 30:2199\u20132208","journal-title":"Eur Radiol"},{"issue":"2","key":"9833_CR7","doi-asserted-by":"publisher","first-page":"2000775","DOI":"10.1183\/13993003.00775-2020","volume":"56","author":"S Wang","year":"2020","unstructured":"Wang S, Zha Y, Li W et al (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56(2):2000775","journal-title":"Eur Respir J"},{"key":"9833_CR8","doi-asserted-by":"publisher","DOI":"10.4414\/smw.2021.20420","volume":"151","author":"NM Zellweger","year":"2021","unstructured":"Zellweger NM, Huber J, Tsakiris DA, Tzankov A, Gebhard CE, Siegemund M (2021) Haemophagocytic lymphohistiocytosis and liver failure-induced massive hyperferritinaemia in a male COVID-19 patient. Swiss Med Wkly 151:w20420","journal-title":"Swiss Med Wkly"},{"issue":"1122\u20131131","key":"9833_CR9","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(1122\u20131131):e1129","journal-title":"Cell"},{"key":"9833_CR10","doi-asserted-by":"publisher","first-page":"E225","DOI":"10.1148\/radiol.2021219004","volume":"299","author":"HX Bai","year":"2021","unstructured":"Bai HX, Wang R, Xiong Z et al (2021) Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 299:E225","journal-title":"Radiology"},{"key":"9833_CR11","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","volume":"11","author":"SA Harmon","year":"2020","unstructured":"Harmon SA, Sanford TH, Xu S et al (2020) Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun 11:4080","journal-title":"Nat Commun"},{"issue":"59\u201372","key":"9833_CR12","volume":"182","author":"B Shen","year":"2020","unstructured":"Shen B, Yi X, Sun Y et al (2020) Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182(59\u201372):e15","journal-title":"Cell"},{"key":"9833_CR13","first-page":"3","volume":"1","author":"TL Wiemken","year":"2017","unstructured":"Wiemken TL, Kelley RR, Fernandez-Botran R et al (2017) Using cluster analysis of cytokines to identify patterns of inflammation in hospitalized patients with community-acquired pneumonia: a pilot study. Univ Louisville J Respir Infect 1:3\u201311","journal-title":"Univ Louisville J Respir Infect"},{"key":"9833_CR14","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1007\/s12250-021-00437-0","volume":"36","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Xu B, Li C et al (2021) A multicenter study of viral aetiology of community-acquired pneumonia in hospitalized children in Chinese Mainland. Virol Sin 36:1543\u20131553","journal-title":"Virol Sin"},{"key":"9833_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2018.2872879","author":"AA Liu","year":"2018","unstructured":"Liu AA, Xu N, Nie WZ, Su YT, Zhang YD (2018) Multi-domain & multi-task learning for human action recognition. IEEE Trans Image Process: a publication of the IEEE Signal Processing Society. https:\/\/doi.org\/10.1109\/tip.2018.2872879","journal-title":"IEEE Trans Image Process: a publication of the IEEE Signal Processing Society"},{"key":"9833_CR16","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1016\/j.ajpath.2019.05.007","volume":"189","author":"S Wang","year":"2019","unstructured":"Wang S, Yang DM, Rong R, Zhan X, Xiao G (2019) Pathology image analysis using segmentation deep learning algorithms. Am J Pathol 189:1686\u20131698","journal-title":"Am J Pathol"},{"key":"9833_CR17","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2021202803","volume":"299","author":"Y Ahn","year":"2021","unstructured":"Ahn Y, Lee SM, Noh HN et al (2021) Use of a commercially available deep learning algorithm to measure the solid portions of lung cancer manifesting as subsolid lesions at CT: comparisons with radiologists and invasive component size at pathologic examination. Radiology 299:202\u2013210","journal-title":"Radiology"},{"key":"9833_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1136\/thoraxjnl-2018-212096","volume":"74","author":"MU Bhuiyan","year":"2019","unstructured":"Bhuiyan MU, Snelling TL, West R et al (2019) The contribution of viruses and bacteria to community-acquired pneumonia in vaccinated children: a case-control study. Thorax 74:261\u2013269","journal-title":"Thorax"},{"key":"9833_CR19","doi-asserted-by":"publisher","first-page":"1764","DOI":"10.1183\/13993003.01144-2016","volume":"48","author":"A Torres","year":"2016","unstructured":"Torres A, Lee N, Cilloniz C, Vila J, Van der Eerden M (2016) Laboratory diagnosis of pneumonia in the molecular age. Eur Respir J 48:1764\u20131778","journal-title":"Eur Respir J"},{"key":"9833_CR20","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3389\/fcimb.2018.00382","volume":"8","author":"RG Wallihan","year":"2018","unstructured":"Wallihan RG, Suarez NM, Cohen DM et al (2018) Molecular distance to health transcriptional score and disease severity in children hospitalized with community-acquired pneumonia. Front Cell Infect Microbiol 8:382","journal-title":"Front Cell Infect Microbiol"},{"key":"9833_CR21","doi-asserted-by":"publisher","DOI":"10.7189\/jogh.07.020408","volume":"7","author":"EW Johansson","year":"2017","unstructured":"Johansson EW, Nsona H, Carvajal-Aguirre L, Amouzou A, Hildenwall H (2017) Determinants of Integrated Management of Childhood Illness (IMCI) non-severe pneumonia classification and care in Malawi health facilities: analysis of a national facility census. J Glob Health 7:020408","journal-title":"J Glob Health"},{"key":"9833_CR22","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.1095","volume":"2","author":"EJ Hwang","year":"2019","unstructured":"Hwang EJ, Park S, Jin KN et al (2019) Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open 2:e191095","journal-title":"JAMA Netw Open"},{"key":"9833_CR23","doi-asserted-by":"publisher","first-page":"4180949","DOI":"10.1155\/2019\/4180949","volume":"2019","author":"O Stephen","year":"2019","unstructured":"Stephen O, Sain M, Maduh UJ, Jeong DU (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019:4180949","journal-title":"J Healthc Eng"},{"key":"9833_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar A, Modzelewski R, Li H, Ruan S (2020) Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: classification and segmentation. Comput Biol Med 126:104037","journal-title":"Comput Biol Med"},{"key":"9833_CR25","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1097\/RLI.0000000000000670","volume":"55","author":"J Wu","year":"2020","unstructured":"Wu J, Wu X, Zeng W et al (2020) Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features. Invest Radiol 55:257\u2013261","journal-title":"Invest Radiol"},{"key":"9833_CR26","doi-asserted-by":"publisher","first-page":"17532","DOI":"10.1038\/s41598-020-74539-2","volume":"10","author":"M Nishio","year":"2020","unstructured":"Nishio M, Noguchi S, Matsuo H, Murakami T (2020) Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Sci Rep 10:17532","journal-title":"Sci Rep"},{"key":"9833_CR27","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2021","unstructured":"Wang J, Sun K, Cheng T et al (2021) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43:3349\u20133364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9833_CR28","doi-asserted-by":"crossref","unstructured":"Chao P, Kao C-Y, Ruan Y-S et al (2019) Hardnet: a low memory traffic network. Proceedings of the IEEE\/CVF international conference on computer vision. arXiv preprint arXiv:3552.3561","DOI":"10.1109\/ICCV.2019.00365"},{"key":"9833_CR29","doi-asserted-by":"publisher","first-page":"16942","DOI":"10.1038\/s41598-020-74164-z","volume":"10","author":"TD Pham","year":"2020","unstructured":"Pham TD (2020) A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Sci Rep 10:16942","journal-title":"Sci Rep"},{"key":"9833_CR30","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.patrec.2020.10.001","volume":"140","author":"M Polsinelli","year":"2020","unstructured":"Polsinelli M, Cinque L, Placidi G (2020) A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognit Lett 140:95\u2013100","journal-title":"Pattern Recognit Lett"},{"key":"9833_CR31","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2020.2994908","volume":"39","author":"J Wang","year":"2020","unstructured":"Wang J, Bao Y, Wen Y et al (2020) Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans Med Imaging 39:2572\u20132583","journal-title":"IEEE Trans Med Imaging"},{"key":"9833_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.753055","volume":"8","author":"YH Zhang","year":"2021","unstructured":"Zhang YH, Hu XF, Ma JC et al (2021) Clinical applicable AI system based on deep learning algorithm for differentiation of pulmonary infectious disease. Front Med (Lausanne) 8:753055","journal-title":"Front Med (Lausanne)"},{"key":"9833_CR33","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1086\/588660","volume":"46","author":"B De Pauw","year":"2008","unstructured":"De Pauw B, Walsh TJ, Donnelly JP et al (2008) Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer\/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC\/MSG) Consensus Group. Clin Infect Dis 46:1813\u20131821","journal-title":"Clin Infect Dis"},{"key":"9833_CR34","doi-asserted-by":"publisher","first-page":"6089","DOI":"10.1007\/s00330-019-06235-3","volume":"29","author":"Y Kunihiro","year":"2019","unstructured":"Kunihiro Y, Tanaka N, Kawano R et al (2019) Differential diagnosis of pulmonary infections in immunocompromised patients using high-resolution computed tomography. Eur Radiol 29:6089\u20136099","journal-title":"Eur Radiol"},{"key":"9833_CR35","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1016\/S2213-2600(18)30286-8","volume":"6","author":"SLF Walsh","year":"2018","unstructured":"Walsh SLF, Calandriello L, Silva M, Sverzellati N (2018) Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med 6:837\u2013845","journal-title":"Lancet Respir Med"},{"key":"9833_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105143","volume":"141","author":"W Chen","year":"2022","unstructured":"Chen W, Han X, Wang J et al (2022) Deep diagnostic agent forest (DDAF): a deep learning pathogen recognition system for pneumonia based on CT. Comput Biol Med 141:105143","journal-title":"Comput Biol Med"},{"key":"9833_CR37","doi-asserted-by":"publisher","first-page":"6517","DOI":"10.1007\/s00330-020-07044-9","volume":"30","author":"Q Ni","year":"2020","unstructured":"Ni Q, Sun ZY, Qi L et al (2020) A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. Eur Radiol 30:6517\u20136527","journal-title":"Eur Radiol"},{"key":"9833_CR38","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1148\/radiol.2021204164","volume":"302","author":"J Choe","year":"2022","unstructured":"Choe J, Hwang HJ, Seo JB et al (2022) Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT. Radiology 302:187\u2013197","journal-title":"Radiology"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09833-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-09833-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09833-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T12:06:07Z","timestamp":1700741167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-023-09833-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":38,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["9833"],"URL":"https:\/\/doi.org\/10.1007\/s00330-023-09833-4","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]},"assertion":[{"value":"8 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Dr. Chen Liu.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies:Southwest Hospital, Third Military Medical University (Army Medical University) for Fang Wang, Xiaoming Li, Peng Wang, Limei Wang, Hongqin Liang, Jian Wang, and Chen Liu. Guizhou University School of Medicine for Ru Wen. Huoshenshan Hospital in Wuhan for Hu Luo. Huiying Medical Technology Co., Ltd. for Dong Liu, Shuai Qi, and Yang Jing. Maternal and Child Health Hospital of Hubei Province for Gang Deng. The 926 Hospital of PLA, Kaiyuan, for Cong Huang. Chongqing Traditional Chinese Medicine Hospital for Tingting Du. All authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because this study used existing CT images including raw data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional review board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}