{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:52:39Z","timestamp":1782233559388,"version":"3.54.5"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s13042-026-03202-5","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:10:18Z","timestamp":1782231018000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved complex words simplification in non-annotated text using a deep-learning-based lexical complexity prediction model"],"prefix":"10.1007","volume":"17","author":[{"given":"Tonghui","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxin","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurice","family":"Mulvenna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinru","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongqiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"3202_CR1","unstructured":"Carroll J, Minnen G, Canning Y, Devlin S, Tait J (1998) Practical simplification of english newspaper text to assist aphasic readers. Proceedings of the AAAI-98 Workshop on integrating artificial intelligence and assistive technology. Association for the Advancement of Artificial Intelligence, pp 7\u201310"},{"key":"3202_CR2","doi-asserted-by":"crossref","unstructured":"Rello L, Bayarri C, G\u00f3rriz A, Baeza-Yates R, Gupta S, Kanvinde G, Saggion H, Bott S, Carlini R, Topac V (2013) Dyswebxia 2.0! more accessible text for people with dyslexia. Proceedings of the 10th international cross-disciplinary conference on web accessibility. pp 1\u20132","DOI":"10.1145\/2461121.2461150"},{"key":"3202_CR3","doi-asserted-by":"crossref","unstructured":"Evans R, Orasan C, Dornescu I (2014) An evaluation of syntactic simplification rules for people with autism. Association for Computational Linguistics","DOI":"10.3115\/v1\/W14-1215"},{"key":"3202_CR4","unstructured":"Devlin S (1998) The use of a psycholinguistic database in the simplification of text for aphasic readers. (Linguistic databases)"},{"key":"3202_CR5","unstructured":"Miwa M, S\u00e6tre R, Miyao Y, Tsujii J (2010) Entity-focused sentence simplification for relation extraction. In: Proceedings of the 23rd international conference on computational linguistics (Coling 2010), pp 788\u2013796"},{"key":"3202_CR6","unstructured":"\u0160tajner S, Popovi\u0107 M (2016) Can text simplification help machine translation? Proceedings of the 19th annual conference of the European association for machine translation. pp 230\u2013242"},{"key":"3202_CR7","doi-asserted-by":"crossref","unstructured":"Xu W, Grishman R (2009) A parse-and-trim approach with information significance for Chinese sentence compression. In: Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+ Sum 2009). pp 48\u201355","DOI":"10.3115\/1708155.1708164"},{"issue":"2","key":"3202_CR8","doi-asserted-by":"publisher","first-page":"689","DOI":"10.64152\/10125\/67046","volume":"8","author":"D Hirsh","year":"1992","unstructured":"Hirsh D (1992) What vocabulary size is needed to read unsimplified texts for pleasure? Read Foreign Lang 8(2):689\u2013696","journal-title":"Read Foreign Lang"},{"key":"3202_CR9","doi-asserted-by":"crossref","unstructured":"Zhang X, Lapata M (2017) Sentence simplification with deep reinforcement learning. EMNLP 2017: conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 584\u2013594","DOI":"10.18653\/v1\/D17-1062"},{"key":"3202_CR10","doi-asserted-by":"crossref","unstructured":"Dong Y, Li Z, Rezagholizadeh M, Cheung JCK (2019) Editnts: an neural programmer-interpreter model for sentence simplification through explicit editing. Proceedings of the 57th annual meeting of the association for computational linguistics. pp 3393\u20133402","DOI":"10.18653\/v1\/P19-1331"},{"key":"3202_CR11","doi-asserted-by":"crossref","unstructured":"Martin L, Fan A, Clergerie \u00c9V, Bordes A, Sagot B (2022) Muss: multilingual unsupervised sentence simplification by mining paraphrases. In: LREC 2022-13th language resources and evaluation conference","DOI":"10.63317\/3kbbve8f2ey4"},{"key":"3202_CR12","doi-asserted-by":"crossref","unstructured":"Vadlamannati S, \u015eahin GG (2023) Metric-based in-context learning: a case study in text simplification. Proceedings of the 16th international natural language generation conference. pp 253\u2013268","DOI":"10.18653\/v1\/2023.inlg-main.18"},{"key":"3202_CR13","unstructured":"Shardlow M, Alva-Manchego F, Batista-Navarro RT, Bott S, Calderon-Ramirez S, Cardon R, Fran\u00e7ois T, Hayakawa A, Horbach A, Huelsing A (2024) The bea 2024 shared task on the multilingual lexical simplification pipeline. Proceedings of the 19th workshop on innovative use of NLP for building educational applications (BEA 2024). pp 571\u2013589"},{"issue":"3","key":"3202_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3758\/BF03197692","volume":"14","author":"K Rayner","year":"1986","unstructured":"Rayner K, Duffy SA (1986) Lexical complexity and fixation times in reading: effects of word frequency, verb complexity, and lexical ambiguity. Mem Cogn 14(3):191\u2013201","journal-title":"Mem Cogn"},{"key":"3202_CR15","doi-asserted-by":"crossref","unstructured":"Desai A, North K, Zampieri M, Homan CM (2021) Lcp-rit at semeval-2021 task 1: exploring linguistic features for lexical complexity prediction. arXiv:2105.08780","DOI":"10.18653\/v1\/2021.semeval-1.67"},{"issue":"2\u20133","key":"3202_CR16","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"ZS Harris","year":"1954","unstructured":"Harris ZS (1954) Distributional structure. Word 10(2\u20133):146\u2013162","journal-title":"Word"},{"key":"3202_CR17","unstructured":"Firth J (1957) A synopsis of linguistic theory, 1930\u20131955. Studies in linguistic analysis. pp 10\u201332"},{"key":"3202_CR18","doi-asserted-by":"crossref","unstructured":"Paetzold G (2021) Utfpr at semeval-2021 task 1: complexity prediction by combining bert vectors and classic features. Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). pp 617\u2013622","DOI":"10.18653\/v1\/2021.semeval-1.78"},{"key":"3202_CR19","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"3202_CR20","doi-asserted-by":"crossref","unstructured":"Flynn R, Shardlow M (2021) Manchester metropolitan at semeval-2021 task 1: convolutional networks for complex word identification. Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021). pp 603\u2013608","DOI":"10.18653\/v1\/2021.semeval-1.76"},{"key":"3202_CR21","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"3202_CR22","unstructured":"Cristea P, Nisioi S (2024) Machine translation for lexical complexity prediction and lexical simplification. Proceedings of the 19th workshop on innovative use of NLP for building educational applications (BEA 2024). pp 610\u2013617"},{"key":"3202_CR23","doi-asserted-by":"crossref","unstructured":"Voskoboinik E (2021) Katildakat at semeval-2021 task 1: lexical complexity prediction of single words and multi-word expressions in english. International workshop on semantic evaluation. Association for Computational Linguistics, pp 700\u2013705","DOI":"10.18653\/v1\/2021.semeval-1.91"},{"key":"3202_CR24","doi-asserted-by":"crossref","unstructured":"Xiang R, Gu J, Chersoni E, Li W, Lu Q, Huang C-R (2021) Polyu cbs-comp at semeval-2021 task 1: Lexical complexity prediction (lcp). Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). pp 565\u2013570","DOI":"10.18653\/v1\/2021.semeval-1.70"},{"key":"3202_CR25","doi-asserted-by":"crossref","unstructured":"Gombert S, Bartsch S (2021) Tuda-ccl at semeval-2021 task 1: using gradient-boosted regression tree ensembles trained on a heterogeneous feature set for predicting lexical complexity. Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). pp 130\u2013137","DOI":"10.18653\/v1\/2021.semeval-1.12"},{"key":"3202_CR26","doi-asserted-by":"crossref","unstructured":"Shardlow M, Evans R, Paetzold GH, Zampieri M (2021) Semeval-2021 task 1: lexical complexity prediction. arXiv:2106.00473","DOI":"10.18653\/v1\/2021.semeval-1.1"},{"key":"3202_CR27","doi-asserted-by":"crossref","unstructured":"Yaseen TB, Ismail Q, Al-Omari S, Al-Sobh E, Abdullah M (2021) Just-blue at semeval-2021 task 1: predicting lexical complexity using bert and roberta pre-trained language models. Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021). pp 661\u2013666","DOI":"10.18653\/v1\/2021.semeval-1.85"},{"key":"3202_CR28","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692"},{"key":"3202_CR29","unstructured":"Goswami D, North K, Zampieri M (2024) Gmu at mlsp 2024: multilingual lexical simplification with transformer models. Proceedings of the 19th workshop on innovative use of NLP for building educational applications (BEA 2024). pp 627\u2013634"},{"key":"3202_CR30","unstructured":"Seneviratne S, Suominen H (2024) Anu at mlsp-2024: prompt-based lexical simplification for english and sinhala. Proceedings of the 19th workshop on innovative use of NLP for building educational applications (BEA 2024). pp 599\u2013604"},{"key":"3202_CR31","unstructured":"Enomoto T, Kim H, Hirasawa T, Nagai Y, Sato A, Nakajima K, Komachi M (2024) Tmu-hit at mlsp 2024: How well can gpt-4 tackle multilingual lexical simplification? Proceedings of the 19th workshop on innovative use of NLP for building educational applications (BEA 2024). pp 590\u2013598"},{"key":"3202_CR32","doi-asserted-by":"crossref","unstructured":"Horn C, Manduca C, Kauchak D (2014) Learning a lexical simplifier using wikipedia. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 2: Short Papers). pp 458\u2013463","DOI":"10.3115\/v1\/P14-2075"},{"key":"3202_CR33","unstructured":"Coster W, Kauchak D (2011) Simple english wikipedia: a new text simplification task. Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. pp 665\u2013669"},{"key":"3202_CR34","doi-asserted-by":"crossref","unstructured":"Glava\u0161 G, \u0160tajner S (2015) Simplifying lexical simplification: Do we need simplified corpora? In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 2: Short Papers). pp 63\u201368","DOI":"10.3115\/v1\/P15-2011"},{"key":"3202_CR35","doi-asserted-by":"crossref","unstructured":"Qiang J, Li Y, Zhu Y, Yuan Y, Wu X (2020) Lexical simplification with pretrained encoders. Proceedings of the AAAI conference on artificial intelligence, vol 34. pp 8649\u20138656","DOI":"10.1609\/aaai.v34i05.6389"},{"key":"3202_CR36","doi-asserted-by":"crossref","unstructured":"Kumar D, Mou L, Golab L, Vechtomova O (2020) Iterative edit-based unsupervised sentence simplification. arXiv:2006.09639","DOI":"10.18653\/v1\/2020.acl-main.707"},{"key":"3202_CR37","unstructured":"Omelianchuk K, Raheja V, Skurzhanskyi O (2021) Text simplification by tagging. arXiv:2103.05070"},{"key":"3202_CR38","doi-asserted-by":"crossref","unstructured":"Omelianchuk K, Atrasevych V, Chernodub A, Skurzhanskyi O (2020) Gector-grammatical error correction: tag, not rewrite. arXiv:2005.12592","DOI":"10.18653\/v1\/2020.bea-1.16"},{"key":"3202_CR39","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"3202_CR40","doi-asserted-by":"crossref","unstructured":"Zhao S, Meng R, He D, Andi S, Bambang P (2018) Integrating transformer and paraphrase rules for sentence simplification. arXiv:1810.11193","DOI":"10.18653\/v1\/D18-1355"},{"key":"3202_CR41","doi-asserted-by":"crossref","unstructured":"Pavlick E, Callison-Burch C (2016) Simple ppdb: a paraphrase database for simplification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: Short Papers). pp 143\u2013148","DOI":"10.18653\/v1\/P16-2024"},{"key":"3202_CR42","unstructured":"Martin L, Sagot B, Clergerie E, Bordes A (2019) Controllable sentence simplification. arXiv:1910.02677"},{"issue":"1","key":"3202_CR43","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/coli_a_00370","volume":"46","author":"F Alva-Manchego","year":"2020","unstructured":"Alva-Manchego F, Scarton C, Specia L (2020) Data-driven sentence simplification: survey and benchmark. Comput Linguist 46(1):135\u2013187","journal-title":"Comput Linguist"},{"issue":"1","key":"3202_CR44","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s10844-024-00882-9","volume":"63","author":"K North","year":"2025","unstructured":"North K, Ranasinghe T, Shardlow M, Zampieri M (2025) Deep learning approaches to lexical simplification: a survey. J Intell Inf Syst 63(1):111\u2013134","journal-title":"J Intell Inf Syst"},{"key":"3202_CR45","doi-asserted-by":"crossref","unstructured":"Maddela M, Xu W (2018) A word-complexity lexicon and a neural readability ranking model for lexical simplification. arXiv:1810.05754","DOI":"10.18653\/v1\/D18-1410"},{"key":"3202_CR46","doi-asserted-by":"publisher","first-page":"227719","DOI":"10.1109\/ACCESS.2020.3041779","volume":"8","author":"Z Tan","year":"2020","unstructured":"Tan Z, Zhang J, He Y, Zhang Y, Xiong G, Liu Y (2020) Short-term load forecasting based on integration of svr and stacking. IEEE Access 8:227719\u2013227728","journal-title":"IEEE Access"},{"key":"3202_CR47","doi-asserted-by":"crossref","unstructured":"Xu J, Deng C, Gao X, Tao D, Li X (2014) Image super-resolution using multi-layer support vector regression. IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5799\u20135803","DOI":"10.1109\/ICASSP.2014.6854715"},{"issue":"11","key":"3202_CR48","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39\u201341","journal-title":"Commun ACM"},{"key":"3202_CR49","doi-asserted-by":"crossref","unstructured":"Fellbaum C (1998) WordNet: an electronic lexical database","DOI":"10.7551\/mitpress\/7287.001.0001"},{"key":"3202_CR50","doi-asserted-by":"publisher","first-page":"904","DOI":"10.3758\/s13428-013-0403-5","volume":"46","author":"M Brysbaert","year":"2014","unstructured":"Brysbaert M, Warriner AB, Kuperman V (2014) Concreteness ratings for 40 thousand generally known english word lemmas. Behav Res Methods 46:904\u2013911","journal-title":"Behav Res Methods"},{"key":"3202_CR51","doi-asserted-by":"publisher","first-page":"978","DOI":"10.3758\/s13428-012-0210-4","volume":"44","author":"V Kuperman","year":"2012","unstructured":"Kuperman V, Stadthagen-Gonzalez H, Brysbaert M (2012) Age-of-acquisition ratings for 30,000 english words. Behav Res Methods 44:978\u2013990","journal-title":"Behav Res Methods"},{"key":"3202_CR52","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"3202_CR53","doi-asserted-by":"crossref","unstructured":"Kim Y, Jernite Y, Sontag D, Rush A (2016) Character-aware neural language models. Proceedings of the AAAI Conference on Artificial Intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.10362"},{"key":"3202_CR54","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. Adv Neural Inf Process Syst 32"},{"key":"3202_CR55","doi-asserted-by":"crossref","unstructured":"Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860","DOI":"10.18653\/v1\/P19-1285"},{"key":"3202_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109298","volume":"252","author":"D Yang","year":"2022","unstructured":"Yang D, Li N, Zou L, Ma H (2022) Lexical semantics enhanced neural word embeddings. Knowl Based Syst 252:109298","journal-title":"Knowl Based Syst"},{"key":"3202_CR57","doi-asserted-by":"crossref","unstructured":"Gupta P, Jaggi M (2021) Obtaining better static word embeddings using contextual embedding models. arXiv:2106.04302","DOI":"10.18653\/v1\/2021.acl-long.408"},{"key":"3202_CR58","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1162\/tacl_a_00107","volume":"4","author":"W Xu","year":"2016","unstructured":"Xu W, Napoles C, Pavlick E, Chen Q, Callison-Burch C (2016) Optimizing statistical machine translation for text simplification. Trans Assoc Comput Linguist 4:401\u2013415","journal-title":"Trans Assoc Comput Linguist"},{"key":"3202_CR59","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv:1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"issue":"140","key":"3202_CR60","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1\u201367","journal-title":"J Mach Learn Res"},{"key":"3202_CR61","doi-asserted-by":"crossref","unstructured":"Kincaid JP, Fishburne Jr RP, Rogers RL, Chissom BS (1975) Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel","DOI":"10.21236\/ADA006655"},{"key":"3202_CR62","doi-asserted-by":"crossref","unstructured":"Alva-Manchego F, Martin L, Scarton C, Specia L (2019) Easse: Easier automatic sentence simplification evaluation. arXiv:1908.04567","DOI":"10.18653\/v1\/D19-3009"},{"key":"3202_CR63","unstructured":"Zhang T, Kishore V, Wu F, Weinberger KQ, Artzi Y (2019) Bertscore: evaluating text generation with bert. arXiv:1904.09675"},{"key":"3202_CR64","doi-asserted-by":"crossref","unstructured":"Pedersen T, Patwardhan S, Michelizzi J (2004) Wordnet: similarity-measuring the relatedness of concepts. Demonstration Papers at HLT-NAACL 2004. pp 38\u201341","DOI":"10.3115\/1614025.1614037"},{"issue":"4","key":"3202_CR65","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1162\/coli_a_00418","volume":"47","author":"F Alva-Manchego","year":"2021","unstructured":"Alva-Manchego F, Scarton C, Specia L (2021) The (un) suitability of automatic evaluation metrics for text simplification. Comput Linguist 47(4):861\u2013889","journal-title":"Comput Linguist"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03202-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03202-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03202-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:11:23Z","timestamp":1782231083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03202-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":65,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["3202"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03202-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]},"assertion":[{"value":"4 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"357"}}