{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:58:06Z","timestamp":1783475886148,"version":"3.55.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100016047","name":"Science Fund of the Republic of Serbia","doi-asserted-by":"crossref","award":["6521051, AI-Clean CaDET"],"award-info":[{"award-number":["6521051, AI-Clean CaDET"]}],"id":[{"id":"10.13039\/501100016047","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09551-y","type":"journal-article","created":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T12:02:22Z","timestamp":1708776142000},"page":"9203-9220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#"],"prefix":"10.1007","volume":"36","author":[{"given":"Aleksandar","family":"Kova\u010devi\u0107","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikola","family":"Luburi\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0351-1183","authenticated-orcid":false,"given":"Jelena","family":"Slivka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona","family":"Proki\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarina-Glorija","family":"Gruji\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dragan","family":"Vidakovi\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goran","family":"Sladi\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"9551_CR1","unstructured":"Fowler M (2018) Refactoring: improving the design of existing code, Addison-Wesley Professional,"},{"key":"9551_CR2","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.jss.2017.12.034","volume":"138","author":"T Sharma","year":"2018","unstructured":"Sharma T, Spinellis D (2018) A survey on software smells. Journal of Systems and Software 138:158\u2013173","journal-title":"Journal of Systems and Software"},{"issue":"3","key":"9551_CR3","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10664-011-9171-y","volume":"17","author":"F Khomh","year":"2012","unstructured":"Khomh F, Di Penta M, Gu\u00e9h\u00e9neuc Y, Antoniol G (2012) An exploratory study of the impact of antipatterns on class change-and fault-proneness. Empirical Software Engineering 17(3):243\u2013275","journal-title":"Empirical Software Engineering"},{"key":"9551_CR4","unstructured":"Martin R (2009) Clean code: a handbook of agile software craftsmanship, Pearson Education,"},{"key":"9551_CR5","first-page":"130","volume":"93","author":"M Hozano","year":"2018","unstructured":"Hozano M, Garcia A, Fonseca B, Costa E (2018) Are you smelling it? Investigating how similar developers detect code smells, Information and Software Technology 93:130\u2013146","journal-title":"Investigating how similar developers detect code smells, Information and Software Technology"},{"key":"9551_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.infsof.2018.12.009","volume":"108","author":"M Azeem","year":"2019","unstructured":"Azeem M, Palomba F, Shi L, Wang Q (2019) Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Information and Software Technology 108:115\u2013138","journal-title":"Information and Software Technology"},{"key":"9551_CR7","doi-asserted-by":"crossref","unstructured":"Lewowski T, Madeyski L (2022) Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review, Developments in Information & Knowledge Management for Business Applications, 285-319","DOI":"10.1007\/978-3-030-77916-0_12"},{"key":"9551_CR8","doi-asserted-by":"crossref","unstructured":"Menshawy R, Yousef A, Salem A (2021) Code Smells and Detection Techniques: A Survey, in International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC),","DOI":"10.1109\/MIUCC52538.2021.9447669"},{"issue":"3","key":"9551_CR9","first-page":"2320","volume":"33","author":"A AbuHassan","year":"2021","unstructured":"AbuHassan A, Alshayeb M, Ghouti L (2021) Software smell detection techniques: A systematic literature review. Journal of Software: Evolution and Process 33(3):2320","journal-title":"Journal of Software: Evolution and Process"},{"key":"9551_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117607","volume":"204","author":"A Kova\u010devi\u0107","year":"2022","unstructured":"Kova\u010devi\u0107 A, Slivka J, Vidakovi\u0107 D, Gruji\u0107 K, Luburi\u0107 N, Proki\u0107 S, Sladi\u0107 G (2022) Automatic detection of Long Method and God Class code smells through neural source code embeddings. Expert Systems with Applications 204:117607","journal-title":"Expert Systems with Applications"},{"key":"9551_CR11","doi-asserted-by":"crossref","unstructured":"Madeyski L, Lewowski T (2020) MLCQ: Industry-relevant code smell data set,, in Proceedings of the Evaluation and Assessment in Software Engineering,","DOI":"10.1145\/3383219.3383264"},{"key":"9551_CR12","doi-asserted-by":"crossref","unstructured":"Lewowski T, Madeyski L (2021) How far are we from reproducible research on code smell detection? A systematic literature review, Information and Software Technology, 106783,","DOI":"10.1016\/j.infsof.2021.106783"},{"key":"9551_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2023.102999","volume":"230","author":"J Slivka","year":"2023","unstructured":"Slivka J, Luburi\u0107 N, Proki\u0107 S, Gruji\u0107 KG, Kova\u010devi\u0107 A, Sladi\u0107 G, Vidakovi\u0107 D (2023) Towards a systematic approach to manual annotation of code smells. Science of Computer Programming 230:102999","journal-title":"Science of Computer Programming"},{"key":"9551_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106333","volume":"125","author":"A Tahir","year":"2020","unstructured":"Tahir A, Dietrich J, Counsell S, Licorish S, Yamashita A (2020) A large scale study on how developers discuss code smells and anti-pattern in stack exchange sites. Information and Software Technology 125:106333","journal-title":"Information and Software Technology"},{"key":"9551_CR15","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781,"},{"key":"9551_CR16","unstructured":"Kenton J, Toutanova L (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in Proceedings of NAACL-HLT,"},{"issue":"4","key":"9551_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3212695","volume":"51","author":"M Allamanis","year":"2018","unstructured":"Allamanis M, Barr E, Devanbu P, Sutton C (2018) A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR) 51(4):1\u201337","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"9551_CR18","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang W, Joty S, Hoi S (2021) CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation, in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing,","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"9551_CR19","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu P (2020) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, Journal of Machine Learning Research, 1-67,"},{"key":"9551_CR20","unstructured":"Lu S, Guo D, Ren S, Huang J, Svyatkovskiy A, Blanco A, Clement C, Drain D, Jiang D, Tang D, Li G (2021) CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, arXiv preprint arXiv:2102.04664,"},{"key":"9551_CR21","unstructured":"Kanade A, Maniatis P, Balakrishnan G, Shi K (2020) Learning and evaluating contextual embedding of source code, in International Conference on Machine Learning PMLR,"},{"key":"9551_CR22","unstructured":"Sharma T, Efstathiou V, Louridas P, Spinellis D (2019) On the feasibility of transfer-learning code smells using deep learning, arXiv preprint arXiv:1904.03031,"},{"key":"9551_CR23","doi-asserted-by":"crossref","unstructured":"Sharma T, Mishra P, Tiwari R (2016) Designite: A software design quality assessment tool, in Proceedings of the 1st International Workshop on Bringing Architectural Design Thinking into Developers\u2019 Daily Activities,","DOI":"10.1145\/2896935.2896938"},{"key":"9551_CR24","doi-asserted-by":"crossref","unstructured":"Velio\u011flu S, Sel\u00e7uk Y (2017) An automated code smell and anti-pattern detection approach, in 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA),","DOI":"10.1109\/SERA.2017.7965737"},{"key":"9551_CR25","doi-asserted-by":"crossref","unstructured":"Tahmid A, Tawhid M, Ahmed S, Sakib K (2017) Code sniffer: a risk based smell detection framework to enhance code quality using static code analysis, International Journal of Software Engineering, Technology and Applications, 2,(1), 41-63","DOI":"10.1504\/IJSETA.2017.086988"},{"key":"9551_CR26","unstructured":"ReSharper: The Visual Studio Extension for .NET Developers by JetBrains, [Online]. Available: https:\/\/www.jetbrains.com\/resharper\/. [Accessed 16 12 2021]"},{"key":"9551_CR27","unstructured":"Improve your .NET code quality with NDepend, [Online]. Available: https:\/\/www.ndepend.com\/. [Accessed 16 12 2021]"},{"key":"9551_CR28","unstructured":"SonarQube - Your teammate for Code Quality and Code Security, [Online]. Available: https:\/\/www.sonarqube.org\/. [Accessed 07 03 2022]"},{"key":"9551_CR29","unstructured":"Brown W, Malveau R, McCormick H, Mowbray T (1998) AntiPatterns: refactoring software, architectures, and projects in crisis, John Wiley & Sons,"},{"key":"9551_CR30","doi-asserted-by":"crossref","unstructured":"Bafandeh Mayvan B, Rasoolzadegan A, Javan A (2020) Jafari, Bad smell detection using quality metrics and refactoring opportunities, Journal of Software: Evolution and Process, 32,(8), 2255,","DOI":"10.1002\/smr.2255"},{"key":"9551_CR31","unstructured":"Sharma T, Kechagia M, Georgiou S, Tiwari R, Sarro F (2021) A Survey on Machine Learning Techniques for Source Code Analysis, arXiv preprint arXiv:2110.09610,"},{"key":"9551_CR32","doi-asserted-by":"crossref","unstructured":"Liu H, Xu Z, Zou Y (2018) Deep learning based feature envy detection, in Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering,","DOI":"10.1145\/3238147.3238166"},{"key":"9551_CR33","unstructured":"Liu H, Jin J, Xu Z, Bu Y, Zou Y, Zhang L (2019) Deep learning based code smell detection, IEEE transactions on Software Engineering,"},{"key":"9551_CR34","doi-asserted-by":"crossref","unstructured":"Hadj-Kacem M, Bouassida N (2019) Improving the Identification of Code Smells by Combining Structural and Semantic Information, in International Conference on Neural Information Processing,","DOI":"10.1007\/978-3-030-36808-1_32"},{"key":"9551_CR35","doi-asserted-by":"crossref","unstructured":"Palomba F, Di Nucci D, Tufano M, Bavota G, Oliveto R, Poshyvanyk D, De Lucia A (2015) Landfill: An open dataset of code smells with public evaluation, in 2015 IEEE\/ACM 12th Working Conference on Mining Software Repositories,","DOI":"10.1109\/MSR.2015.69"},{"key":"9551_CR36","doi-asserted-by":"crossref","unstructured":"Guo X, Shi C, and H. Jiang, (2019) Deep semantic-Based Feature Envy Identification, in Proceedings of the 11th Asia-Pacific Symposium on Internetware,","DOI":"10.1145\/3361242.3361257"},{"issue":"3","key":"9551_CR37","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s10664-015-9378-4","volume":"21","author":"F Fontana","year":"2016","unstructured":"Fontana F, M\u00e4ntyl\u00e4 M, Zanoni M, Marino A (2016) Comparing and experimenting machine learning techniques for code smell detection. Empirical Software Engineering 21(3):1143\u20131191","journal-title":"Empirical Software Engineering"},{"key":"9551_CR38","doi-asserted-by":"crossref","unstructured":"Di Nucci D, Palomba F, Tamburri D, Serebrenik A, De Lucia A (2018) Detecting code smells using machine learning techniques: are we there yet?, in 2018 ieee 25th international conference on software analysis, evolution and reengineering (saner),","DOI":"10.1109\/SANER.2018.8330266"},{"key":"9551_CR39","doi-asserted-by":"crossref","unstructured":"Zhang Y, Dong C (2021) MARS: Detecting brain class\/method code smell based on metric-attention mechanism and residual network, Journal of Software: Evolution and Process, e2403,","DOI":"10.1002\/smr.2403"},{"issue":"2","key":"9551_CR40","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s13369-016-2238-8","volume":"42","author":"G Rasool","year":"2017","unstructured":"Rasool G, Arshad Z (2017) A lightweight approach for detection of code smells. Arabian Journal for Science and Engineering 42(2):483\u2013506","journal-title":"Arabian Journal for Science and Engineering"},{"key":"9551_CR41","first-page":"1","volume":"18","author":"G Lemaitre","year":"2017","unstructured":"Lemaitre G, Nogueira F, Aridas C (2017) Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research 18:1\u20135","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"9551_CR42","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TSE.2011.9","volume":"38","author":"H Liu","year":"2011","unstructured":"Liu H, Ma Z, Shao W, Niu Z (2011) Schedule of bad smell detection and resolution: A new way to save effort. IEEE transactions on Software Engineering 38(1):220\u2013235","journal-title":"IEEE transactions on Software Engineering"},{"key":"9551_CR43","unstructured":"Padilha J, Pereira J, Figueiredo E, Almeida J, Garcia A, Sant\u2019Anna C On the effectiveness of concern metrics to detect code smells: An empirical study, in International Conference on Advanced Information Systems Engineering"},{"key":"9551_CR44","doi-asserted-by":"crossref","unstructured":"Proki\u0107 S, Gruji\u0107 K, Luburi\u0107 N, Slivka J, Kova\u010devi\u0107 A, Vidakovi\u0107 D, Sladi\u0107 G Clean Code and Design Educational Tool,, in 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)","DOI":"10.23919\/MIPRO52101.2021.9597196"},{"key":"9551_CR45","doi-asserted-by":"crossref","unstructured":"Alon U, Zilberstein M, Levy O, Yahav E (2019) code2vec: Learning distributed representations of code, in Proceedings of the ACM on Programming Languages, 3(POPL),","DOI":"10.1145\/3290353"},{"issue":"05","key":"9551_CR46","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1142\/S0218194020500230","volume":"30","author":"Y Hussain","year":"2020","unstructured":"Hussain Y, Huang Z, Zhou Y, Wang S (2020) Deep transfer learning for source code modeling. International Journal of Software Engineering and Knowledge Engineering 30(05):649\u2013668","journal-title":"International Journal of Software Engineering and Knowledge Engineering"},{"key":"9551_CR47","doi-asserted-by":"crossref","unstructured":"Compton R, Frank E, Patros P, Koay A (2020) Embedding java classes with code2vec: Improvements from variable obfuscation, in Proceedings of the 17th International Conference on Mining Software Repositories,","DOI":"10.1145\/3379597.3387445"},{"key":"9551_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110693","volume":"169","author":"F Pecorelli","year":"2020","unstructured":"Pecorelli F, Di Nucci D, De Roover C, De Lucia A (2020) A large empirical assessment of the role of data balancing in machine-learning-based code smell detection. Journal of Systems and Software 169:110693","journal-title":"Journal of Systems and Software"},{"key":"9551_CR49","unstructured":"Ng A Machine learning yearning: Technical strategy for ai engineers in the era of deep learning, 2019. [Online]. Available: https:\/\/www.mlyearning.org. [Accessed 21 10 2022]"},{"key":"9551_CR50","unstructured":"GitHub, Your AI pair programmer, [Online]. Available: https:\/\/copilot.github.com\/. [Accessed 07 03 2022]"},{"key":"9551_CR51","unstructured":"Trifu A, Marinescu R (2005) Diagnosing design problems in object oriented systems, in 12th Working Conference on Reverse Engineering (WCRE\u201905),"},{"key":"9551_CR52","unstructured":"Macia I, Garcia J, Popescu D, Garcia A, Medvidovic N, von Staa A Are automatically-detected code anomalies relevant to architectural modularity? An exploratory analysis of evolving systems, in Proceedings of the 11th annual international conf"},{"key":"9551_CR53","doi-asserted-by":"crossref","unstructured":"Souza P, Sousa B, Ferreira K, Bigonha M (2017) Applying software metric thresholds for detection of bad smells, in Proceedings of the 11th Brazilian Symposium on Software Components, Architectures, and Reuse,","DOI":"10.1145\/3132498.3134268"},{"key":"9551_CR54","doi-asserted-by":"crossref","unstructured":"Kiefer C, Bernstein A, Tappolet J (2007) Mining software repositories with isparol and a software evolution ontology, in Fourth International Workshop on Mining Software Repositories (MSR\u201907: ICSE Workshops 2007),","DOI":"10.1109\/MSR.2007.21"},{"key":"9551_CR55","doi-asserted-by":"crossref","unstructured":"Danphitsanuphan P, Suwantada T (2012) Code smell detecting tool and code smell-structure bug relationship, in 2012 Spring Congress on Engineering and Technology,","DOI":"10.1109\/SCET.2012.6342082"},{"key":"9551_CR56","doi-asserted-by":"crossref","unstructured":"Fard A, Mesbah A (2013) Jsnose: Detecting javascript code smells, in 2013 IEEE 13th international working conference on Source Code Analysis and Manipulation (SCAM),","DOI":"10.1109\/SCAM.2013.6648192"},{"issue":"1","key":"9551_CR57","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TSE.2009.50","volume":"36","author":"N Moha","year":"2009","unstructured":"Moha N, Gu\u00e9h\u00e9neuc Y, Duchien L, Le Meur A (2009) Decor: A method for the specification and detection of code and design smells. IEEE Transactions on Software Engineering 36(1):20\u201336","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9551_CR58","doi-asserted-by":"crossref","unstructured":"Lerthathairat P, Prompoon N (2011) An approach for source code classification to enhance maintainability, in 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE),","DOI":"10.1109\/JCSSE.2011.5930141"},{"key":"9551_CR59","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J (2011) Scikit-learn: Machine learning in Python. Journal of machine Learning research 12:2825\u20132830","journal-title":"Journal of machine Learning research"},{"key":"9551_CR60","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,","DOI":"10.1145\/2939672.2939785"},{"key":"9551_CR61","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush A, Gulin A (2018) CatBoost: unbiased boosting with categorical features, Advances in neural information processing systems, 31,"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09551-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09551-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T01:09:58Z","timestamp":1715389798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09551-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,24]]},"references-count":61,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9551"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09551-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.19682754.v2","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.19682754.v1","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,24]]},"assertion":[{"value":"23 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors report there are no competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}