{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:34:02Z","timestamp":1776850442636,"version":"3.51.2"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100007698","name":"University of Florida","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007698","id-type":"DOI","asserted-by":"crossref"}]},{"name":"-National Research, Development and Innovation Office of Hungary","award":["K_20 134260"],"award-info":[{"award-number":["K_20 134260"]}]},{"name":"-National Research, Development and Innovation Office of Hungary","award":["PD_20 134416"],"award-info":[{"award-number":["PD_20 134416"]}]},{"name":"-Hungarian Academy of Sciences: J\u00e1nos Bolyai Research Scholarship"},{"name":"-Ministry for Innovation and Technology of Hungary","award":["\u00daNKP-21-5"],"award-info":[{"award-number":["\u00daNKP-21-5"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s10822-022-00444-7","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T02:03:40Z","timestamp":1647309820000},"page":"157-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Extended continuous similarity indices: theory and application for QSAR descriptor selection"],"prefix":"10.1007","volume":"36","author":[{"given":"Anita","family":"R\u00e1cz","sequence":"first","affiliation":[]},{"given":"Timothy B.","family":"Dunn","sequence":"additional","affiliation":[]},{"given":"D\u00e1vid","family":"Bajusz","sequence":"additional","affiliation":[]},{"given":"Taewon D.","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2121-4449","authenticated-orcid":false,"given":"Ram\u00f3n Alain","family":"Miranda-Quintana","sequence":"additional","affiliation":[]},{"given":"K\u00e1roly","family":"H\u00e9berger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"444_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/B978-0-12-409547-2.12345-5","volume-title":"Comprehensive medicinal chemistry III","author":"D Bajusz","year":"2017","unstructured":"Bajusz D, R\u00e1cz A, H\u00e9berger K (2017) Chemical data formats, fingerprints, and other molecular descriptions for database analysis and searching. In: Chackalamannil S, Rotella DP, Ward SE (eds) Comprehensive medicinal chemistry III. Elsevier, Oxford, pp 329\u2013378"},{"key":"444_CR2","doi-asserted-by":"publisher","first-page":"3204","DOI":"10.1039\/b409813g","volume":"2","author":"A Bender","year":"2004","unstructured":"Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204\u20133218","journal-title":"Org Biomol Chem"},{"key":"444_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13321-015-0069-3","volume":"7","author":"D Bajusz","year":"2015","unstructured":"Bajusz D, R\u00e1cz A, H\u00e9berger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7:20. https:\/\/doi.org\/10.1186\/s13321-015-0069-3","journal-title":"J Cheminform"},{"key":"444_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/J.NEUCOM.2017.06.053","volume":"267","author":"A Saxena","year":"2017","unstructured":"Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664\u2013681. https:\/\/doi.org\/10.1016\/J.NEUCOM.2017.06.053","journal-title":"Neurocomputing"},{"key":"444_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1021\/ci900419k","volume":"50","author":"H Geppert","year":"2010","unstructured":"Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50:205\u2013216. https:\/\/doi.org\/10.1021\/ci900419k","journal-title":"J Chem Inf Model"},{"key":"444_CR6","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.drudis.2007.01.011","volume":"12","author":"H Eckert","year":"2007","unstructured":"Eckert H, Bajorath J (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today 12:225\u2013233. https:\/\/doi.org\/10.1016\/j.drudis.2007.01.011","journal-title":"Drug Discov Today"},{"key":"444_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/aris.2009.1440430108","volume":"43","author":"P Willett","year":"2009","unstructured":"Willett P (2009) Similarity methods in chemoinformatics. Annu Rev Inf Sci Technol 43:1\u2013117. https:\/\/doi.org\/10.1002\/aris.2009.1440430108","journal-title":"Annu Rev Inf Sci Technol"},{"key":"444_CR8","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1016\/j.drudis.2006.10.005","volume":"11","author":"P Willett","year":"2006","unstructured":"Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046\u20131053. https:\/\/doi.org\/10.1016\/j.drudis.2006.10.005","journal-title":"Drug Discov Today"},{"key":"444_CR9","doi-asserted-by":"publisher","first-page":"e201302002","DOI":"10.5936\/csbj.201302002","volume":"5","author":"P Willett","year":"2013","unstructured":"Willett P (2013) Fusing similarity rankings in ligand-based virtual screening. Comput Struct Biotechnol J 5:e201302002. https:\/\/doi.org\/10.5936\/csbj.201302002","journal-title":"Comput Struct Biotechnol J"},{"key":"444_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci300547g","volume":"53","author":"P Willett","year":"2013","unstructured":"Willett P (2013) Combination of similarity rankings using data fusion. J Chem Inf Model 53:1\u201310. https:\/\/doi.org\/10.1021\/ci300547g","journal-title":"J Chem Inf Model"},{"key":"444_CR11","doi-asserted-by":"publisher","first-page":"2884","DOI":"10.1021\/ci300261r","volume":"52","author":"R Todeschini","year":"2012","unstructured":"Todeschini R, Consonni V, Xiang H et al (2012) Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J Chem Inf Model 52:2884\u20132901. https:\/\/doi.org\/10.1021\/ci300261r","journal-title":"J Chem Inf Model"},{"key":"444_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11306-018-1327-y","author":"A R\u00e1cz","year":"2018","unstructured":"R\u00e1cz A, Andri\u0107 F, Bajusz D, H\u00e9berger K (2018) Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. Metabolomics. https:\/\/doi.org\/10.1007\/s11306-018-1327-y","journal-title":"Metabolomics"},{"key":"444_CR13","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-018-0302-y","volume":"10","author":"A R\u00e1cz","year":"2018","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2018) Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. J Cheminform 10:48. https:\/\/doi.org\/10.1186\/s13321-018-0302-y","journal-title":"J Cheminform"},{"key":"444_CR14","doi-asserted-by":"publisher","first-page":"2060017","DOI":"10.1002\/minf.202060017","volume":"40","author":"RA Miranda-Quintana","year":"2021","unstructured":"Miranda-Quintana RA, Bajusz D, R\u00e1cz A, H\u00e9berger K (2021) Differential consistency analysis: which similarity measures can be applied in drug discovery? Mol Inform 40:2060017. https:\/\/doi.org\/10.1002\/minf.202060017","journal-title":"Mol Inform"},{"key":"444_CR15","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13321-021-00505-3","volume":"13","author":"RA Miranda-Quintana","year":"2021","unstructured":"Miranda-Quintana RA, Bajusz D, R\u00e1cz A, H\u00e9berger K (2021) Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: theory and characteristics. J Cheminform 13:32. https:\/\/doi.org\/10.1186\/s13321-021-00505-3","journal-title":"J Cheminform"},{"key":"444_CR16","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13321-021-00504-4","volume":"13","author":"RA Miranda-Quintana","year":"2021","unstructured":"Miranda-Quintana RA, R\u00e1cz A, Bajusz D, H\u00e9berger K (2021) Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection. J Cheminform 13:33. https:\/\/doi.org\/10.1186\/s13321-021-00504-4","journal-title":"J Cheminform"},{"key":"444_CR17","doi-asserted-by":"publisher","DOI":"10.1021\/ACS.JCIM.1C01013","author":"TB Dunn","year":"2021","unstructured":"Dunn TB, Seabra GM, Kim TD et al (2021) Diversity and chemical library networks of large data sets. J Chem Inf Model. https:\/\/doi.org\/10.1021\/ACS.JCIM.1C01013","journal-title":"J Chem Inf Model"},{"key":"444_CR18","doi-asserted-by":"publisher","DOI":"10.1101\/2021.08.08.455555","author":"L Chang","year":"2021","unstructured":"Chang L, Perez A, Miranda-Quintana RA (2021) Improving the analysis of biological ensembles through extended similarity measures. BioRxiv. https:\/\/doi.org\/10.1101\/2021.08.08.455555","journal-title":"BioRxiv"},{"key":"444_CR19","doi-asserted-by":"publisher","DOI":"10.33774\/CHEMRXIV-2021-0PQ98","author":"A Flores-Padilla","year":"2021","unstructured":"Flores-Padilla A, Eur\u00eddice Ju\u00e1rez-Mercado K, Naveja JJ et al (2021) Chemoinformatic characterization of synthetic screening libraries focused on epigenetic targets. ChemRxiv. https:\/\/doi.org\/10.33774\/CHEMRXIV-2021-0PQ98","journal-title":"ChemRxiv"},{"key":"444_CR20","doi-asserted-by":"publisher","first-page":"3628","DOI":"10.1016\/j.csbj.2021.06.021","volume":"19","author":"D Bajusz","year":"2021","unstructured":"Bajusz D, Miranda-Quintana RA, R\u00e1cz A, H\u00e9berger K (2021) Extended many-item similarity indices for sets of nucleotide and protein sequences. Comput Struct Biotechnol J 19:3628\u20133639. https:\/\/doi.org\/10.1016\/j.csbj.2021.06.021","journal-title":"Comput Struct Biotechnol J"},{"key":"444_CR21","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977\u20135010. https:\/\/doi.org\/10.1021\/jm4004285","journal-title":"J Med Chem"},{"key":"444_CR22","doi-asserted-by":"publisher","first-page":"126001","DOI":"10.1289\/EHP3264","volume":"126","author":"G Piir","year":"2018","unstructured":"Piir G, Kahn I, Garc\u00eda-Sosa AT et al (2018) Best practices for QSAR model reporting: physical and chemical properties, ecotoxicity, environmental fate, human health, and toxicokinetics endpoints. Environ Health Perspect 126:126001. https:\/\/doi.org\/10.1289\/EHP3264","journal-title":"Environ Health Perspect"},{"key":"444_CR23","doi-asserted-by":"publisher","first-page":"104170","DOI":"10.1016\/J.CHEMOLAB.2020.104170","volume":"206","author":"ZY Algamal","year":"2020","unstructured":"Algamal ZY, Qasim MK, Lee MH, Mohammad Ali HT (2020) High-dimensional QSAR\/QSPR classification modeling based on improving pigeon optimization algorithm. Chemom Intell Lab Syst 206:104170. https:\/\/doi.org\/10.1016\/J.CHEMOLAB.2020.104170","journal-title":"Chemom Intell Lab Syst"},{"key":"444_CR24","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100\u2013D1107. https:\/\/doi.org\/10.1093\/nar\/gkr777","journal-title":"Nucleic Acids Res"},{"key":"444_CR25","first-page":"217","volume-title":"Annual reports in computational chemistry","author":"EE Bolton","year":"2008","unstructured":"Bolton EE, Wang Y, Thiessen PA, Bryant SH (2008) Chapter 12\u2014PubChem: integrated platform of small molecules and biological activities. Annual reports in computational chemistry. Elsevier, Amsterdam, pp 217\u2013241"},{"key":"444_CR26","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1002\/cem.1360","volume":"24","author":"CM Andersen","year":"2010","unstructured":"Andersen CM, Bro R (2010) Variable selection in regression\u2014a tutorial. J Chemom 24:728\u2013737. https:\/\/doi.org\/10.1002\/cem.1360","journal-title":"J Chemom"},{"key":"444_CR27","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/J.CHROMA.2007.04.025","volume":"1158","author":"R Leardi","year":"2007","unstructured":"Leardi R (2007) Genetic algorithms in chemistry. J Chromatogr A 1158:226\u2013233. https:\/\/doi.org\/10.1016\/J.CHROMA.2007.04.025","journal-title":"J Chromatogr A"},{"key":"444_CR28","doi-asserted-by":"publisher","first-page":"636","DOI":"10.5740\/JAOACINT.SGE_GOODARZI","volume":"95","author":"M Goodarzi","year":"2012","unstructured":"Goodarzi M, Dejaegher B, Vander HY (2012) Feature selection methods in QSAR studies. J AOAC Int 95:636\u2013651. https:\/\/doi.org\/10.5740\/JAOACINT.SGE_GOODARZI","journal-title":"J AOAC Int"},{"key":"444_CR29","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1021\/CI400573C","volume":"54","author":"M Eklund","year":"2014","unstructured":"Eklund M, Norinder U, Boyer S, Carlsson L (2014) Choosing feature selection and learning algorithms in QSAR. J Chem Inf Model 54:837\u2013843. https:\/\/doi.org\/10.1021\/CI400573C","journal-title":"J Chem Inf Model"},{"key":"444_CR30","unstructured":"National Center for Biotechnology Information. PubChem Database. Source=NCGC, AID=1851"},{"key":"444_CR31","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1007\/s11030-021-10239-x","volume":"25","author":"A R\u00e1cz","year":"2021","unstructured":"R\u00e1cz A, Bajusz D, Miranda-Quintana RA, H\u00e9berger K (2021) Machine learning models for classification tasks related to drug safety. Mol Divers 25:1409\u20131424. https:\/\/doi.org\/10.1007\/s11030-021-10239-x","journal-title":"Mol Divers"},{"key":"444_CR32","first-page":"237","volume":"56","author":"A Mauri","year":"2006","unstructured":"Mauri A, Consonni V, Pavan M, Todeschini R (2006) Dragon software: an easy approach to molecular descriptor calculations. MATCH Commun Math Comput Chem 56:237\u2013248","journal-title":"MATCH Commun Math Comput Chem"},{"key":"444_CR33","unstructured":"(2018) Dragon 7.0, Kode Cheminformatics. Dragon 70, Kode Cheminformatics"},{"key":"444_CR34","doi-asserted-by":"publisher","first-page":"1800154","DOI":"10.1002\/minf.201800154","volume":"38","author":"A R\u00e1cz","year":"2019","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2019) Intercorrelation limits in molecular descriptor preselection for QSAR\/QSPR. Mol Inform 38:1800154. https:\/\/doi.org\/10.1002\/minf.201800154","journal-title":"Mol Inform"},{"key":"444_CR35","doi-asserted-by":"publisher","first-page":"1898","DOI":"10.1039\/C5MD00253B","volume":"6","author":"D Bajusz","year":"2015","unstructured":"Bajusz D, Ferenczy GG, Keser\u0171 GM (2015) Property-based characterization of kinase-like ligand space for library design and virtual screening. Med Chem Commun 6:1898\u20131904. https:\/\/doi.org\/10.1039\/C5MD00253B","journal-title":"Med Chem Commun"},{"key":"444_CR36","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10822-014-9804-5","volume":"29","author":"AA Kelemen","year":"2015","unstructured":"Kelemen AA, Ferenczy GG, Keser\u0171 GM (2015) A desirability function-based scoring scheme for selecting fragment-like class A aminergic GPCR ligands. J Comput Aided Mol Des 29:59\u201366. https:\/\/doi.org\/10.1007\/s10822-014-9804-5","journal-title":"J Comput Aided Mol Des"},{"key":"444_CR37","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.trac.2009.09.009","volume":"29","author":"K H\u00e9berger","year":"2010","unstructured":"H\u00e9berger K (2010) Sum of ranking differences compares methods or models fairly. TrAC Trends Anal Chem 29:101\u2013109. https:\/\/doi.org\/10.1016\/j.trac.2009.09.009","journal-title":"TrAC Trends Anal Chem"},{"key":"444_CR38","doi-asserted-by":"publisher","first-page":"e3011","DOI":"10.1002\/cem.3011","volume":"32","author":"L Sipos","year":"2018","unstructured":"Sipos L, Gere A, Popp J, Kov\u00e1cs S (2018) A novel ranking distance measure combining Cayley and Spearman footrule metrics. J Chemom 32:e3011. https:\/\/doi.org\/10.1002\/cem.3011","journal-title":"J Chemom"},{"key":"444_CR39","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1002\/cem.1320","volume":"25","author":"K H\u00e9berger","year":"2011","unstructured":"H\u00e9berger K, Koll\u00e1r-Hunek K (2011) Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers. J Chemom 25:151\u2013158. https:\/\/doi.org\/10.1002\/cem.1320","journal-title":"J Chemom"},{"key":"444_CR40","doi-asserted-by":"publisher","first-page":"e3104","DOI":"10.1002\/CEM.3104","volume":"33","author":"K H\u00e9berger","year":"2019","unstructured":"H\u00e9berger K, Koll\u00e1r-Hunek K (2019) Comparison of validation variants by sum of ranking differences and ANOVA. J Chemom 33:e3104. https:\/\/doi.org\/10.1002\/CEM.3104","journal-title":"J Chemom"},{"key":"444_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMAG.2018.2836327","volume":"54","author":"JM Lourenco","year":"2018","unstructured":"Lourenco JM, Lebensztajn L (2018) Post-Pareto optimality analysis with sum of ranking differences. IEEE Trans Magn 54:1\u201310. https:\/\/doi.org\/10.1109\/TMAG.2018.2836327","journal-title":"IEEE Trans Magn"},{"key":"444_CR42","doi-asserted-by":"publisher","first-page":"128617","DOI":"10.1016\/j.foodchem.2020.128617","volume":"344","author":"A Gere","year":"2021","unstructured":"Gere A, R\u00e1cz A, Bajusz D, H\u00e9berger K (2021) Multicriteria decision making for evergreen problems in food science by sum of ranking differences. Food Chem 344:128617. https:\/\/doi.org\/10.1016\/j.foodchem.2020.128617","journal-title":"Food Chem"},{"key":"444_CR43","doi-asserted-by":"publisher","first-page":"3119","DOI":"10.3390\/APP11073119","volume":"11","author":"CL Saratxaga","year":"2021","unstructured":"Saratxaga CL, Bote J, Ortega-Mor\u00e1n JF et al (2021) Characterization of optical coherence tomography images for colon lesion differentiation under deep learning. Appl Sci 11:3119. https:\/\/doi.org\/10.3390\/APP11073119","journal-title":"Appl Sci"},{"key":"444_CR44","doi-asserted-by":"publisher","first-page":"101133","DOI":"10.1016\/J.JOI.2021.101133","volume":"15","author":"BR Sziklai","year":"2021","unstructured":"Sziklai BR (2021) Ranking institutions within a discipline: the steep mountain of academic excellence. J Informetr 15:101133. https:\/\/doi.org\/10.1016\/J.JOI.2021.101133","journal-title":"J Informetr"},{"key":"444_CR45","first-page":"2","volume":"36","author":"C West","year":"2018","unstructured":"West C (2018) Statistics for analysts who hate statistics, part VII: sum of ranking differences (SRD). LCGC North Am 36:2\u20136","journal-title":"LCGC North Am"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-022-00444-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-022-00444-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-022-00444-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T04:07:37Z","timestamp":1649477257000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-022-00444-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["444"],"URL":"https:\/\/doi.org\/10.1007\/s10822-022-00444-7","relation":{},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3]]},"assertion":[{"value":"11 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2022","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}