{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:54:49Z","timestamp":1773658489326,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":[[2026,2]]},"DOI":"10.1007\/s13042-025-02880-x","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T06:12:28Z","timestamp":1770012748000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting AI-generated essays using fine-tuned XLNet-CNN hybrid techniques: a study of the academic integrity challenge"],"prefix":"10.1007","volume":"17","author":[{"given":"Manish","family":"Prajapati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santos Kumar","family":"Baliarsingh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prabhu Prasad","family":"Dev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"2880_CR1","volume-title":"ChatGPT parent Open AI Gets \u2018game changing\u2019multibillion-dollar boost from Microsoft","author":"D Saul","year":"2023","unstructured":"Saul D (2023) ChatGPT parent Open AI Gets \u2018game changing\u2019multibillion-dollar boost from Microsoft. Forbes, Jersey"},{"issue":"4","key":"2880_CR2","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1007\/s00146-022-01397-z","volume":"38","author":"P Fyfe","year":"2023","unstructured":"Fyfe P (2023) How to cheat on your final paper: assigning ai for student writing. AI & Soc 38(4):1395\u20131405","journal-title":"AI & Soc"},{"issue":"3","key":"2880_CR3","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6552\/acc5cf","volume":"58","author":"W Yeadon","year":"2023","unstructured":"Yeadon W, Inyang O-O, Mizouri A, Peach A, Testrow CP (2023) The death of the short-form physics essay in the coming ai revolution. Phys Educ 58(3):035027","journal-title":"Phys Educ"},{"issue":"4","key":"2880_CR4","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1007\/s40593-022-00300-7","volume":"32","author":"M Sharples","year":"2022","unstructured":"Sharples M (2022) Automated essay writing: an AIED opinion. Int J Artif Intell Educ 32(4):1119\u20131126","journal-title":"Int J Artif Intell Educ"},{"issue":"6","key":"2880_CR5","doi-asserted-by":"publisher","first-page":"3197","DOI":"10.1007\/s11845-023-03377-8","volume":"192","author":"E Waisberg","year":"2023","unstructured":"Waisberg E, Ong J, Masalkhi M, Kamran SA, Zaman N, Sarker P, Lee AG, Tavakkoli A (2023) Gpt-4: a new era of artificial intelligence in medicine. Ir J Med Sci (1971) 192(6):3197\u20133200","journal-title":"Ir J Med Sci (1971)"},{"key":"2880_CR6","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi L, Chiriatti M (2020) Gpt-3: its nature, scope, limits, and consequences. Mind Mach 30:681\u2013694","journal-title":"Mind Mach"},{"key":"2880_CR7","first-page":"1","volume-title":"2025 19th International conference on ubiquitous information management and communication (IMCOM)","author":"A Yadagiri","year":"2025","unstructured":"Yadagiri A, Pakray P (2025) Deep learning strategies for identifying machine-generated text. 2025 19th International conference on ubiquitous information management and communication (IMCOM). IEEE, Geneva, pp 1\u20138"},{"issue":"4","key":"2880_CR8","doi-asserted-by":"publisher","first-page":"28697","DOI":"10.1097\/MD.0000000000028697","volume":"101","author":"C-Y Lin","year":"2022","unstructured":"Lin C-Y, Chien T-W, Chen Y-H, Lee Y-L, Su S-B (2022) An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft excel: development and usability study. Medicine 101(4):28697","journal-title":"Medicine"},{"key":"2880_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404:132306","journal-title":"Physica D"},{"key":"2880_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106682","volume":"186","author":"X Song","year":"2020","unstructured":"Song X, Liu Y, Xue L, Wang J, Zhang J, Wang J, Jiang L, Cheng Z (2020) Time-series well performance prediction based on long short-term memory (LSTM) neural network model. J Petrol Sci Eng 186:106682","journal-title":"J Petrol Sci Eng"},{"key":"2880_CR11","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338","journal-title":"Neurocomputing"},{"key":"2880_CR12","unstructured":"Rana R (2016) Gated recurrent unit (gru) for emotion classification from noisy speech. arXiv preprint arXiv:1612.07778"},{"key":"2880_CR13","doi-asserted-by":"publisher","first-page":"180544","DOI":"10.1109\/ACCESS.2020.3028281","volume":"8","author":"M Alhussein","year":"2020","unstructured":"Alhussein M, Aurangzeb K, Haider SI (2020) Hybrid CNN-LSTM model for short-term individual household load forecasting. Ieee Access 8:180544\u2013180557","journal-title":"Ieee Access"},{"key":"2880_CR14","first-page":"19","volume-title":"Operations research forum","author":"GJ Saqware","year":"2024","unstructured":"Saqware GJ (2024) Hybrid deep learning model integrating attention mechanism for the accurate prediction and forecasting of the cryptocurrency market. Operations research forum. Springer, Cham, p 19"},{"key":"2880_CR15","doi-asserted-by":"publisher","first-page":"1292010","DOI":"10.3389\/fnhum.2023.1292010","volume":"17","author":"NA Semary","year":"2023","unstructured":"Semary NA, Ahmed W, Amin K, P\u0142awiak P, Hammad M (2023) Improving sentiment classification using a Roberta-based hybrid model. Front Hum Neurosci 17:1292010","journal-title":"Front Hum Neurosci"},{"key":"2880_CR16","unstructured":"Yang Z (2019) Xlnet: generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237"},{"key":"2880_CR17","doi-asserted-by":"crossref","unstructured":"Gehrmann S, Strobelt H, Rush AM (2019) Gltr: statistical detection and visualization of generated text. arXiv preprint arXiv:1906.04043","DOI":"10.18653\/v1\/P19-3019"},{"key":"2880_CR18","unstructured":"Sadasivan VS, Kumar A, Balasubramanian S, Wang W, Feizi S (2023) Can ai-generated text be reliably detected? arXiv preprint arXiv:2303.11156"},{"key":"2880_CR19","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-031-02165-7","volume-title":"Neural network methods in natural language processing","author":"Y Goldberg","year":"2017","unstructured":"Goldberg Y, Hirst G (2017) Neural network methods in natural language processing. Morgan and Claypool Publishers, San Rafael, p 69"},{"key":"2880_CR20","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"2880_CR21","doi-asserted-by":"crossref","unstructured":"Ling W, Dyer C, Black AW, Trancoso I (2015) Two\/too simple adaptations of word2vec for syntax problems. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1299\u20131304","DOI":"10.3115\/v1\/N15-1142"},{"key":"2880_CR22","unstructured":"Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:1802.05365"},{"key":"2880_CR23","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"2880_CR24","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":"2880_CR25","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"2880_CR26","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding with unsupervised learning. 2018. URL: https:\/\/openai. com\/research\/language-unsupervised"},{"key":"2880_CR27","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2019) Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"2880_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 preprint arXiv:1907.11692"},{"issue":"140","key":"2880_CR29","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":"2880_CR30","unstructured":"Hou X, Zhao Y, Liu Y, Yang Z, Wang K, Li L, Luo X, Lo D, Grundy J, Wang H (2023) Large language models for software engineering: a systematic literature review. arXiv preprint arXiv:2308.10620"},{"key":"2880_CR31","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"2880_CR32","unstructured":"Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, Almeida D, Altenschmidt J, Altman S, Anadkat S, et al (2023) Gpt-4 technical report. arXiv preprint arXiv:2303.08774"},{"key":"2880_CR33","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F, et al (2023) Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"2880_CR34","doi-asserted-by":"crossref","unstructured":"Reynolds L, McDonell K (2021) Prompt programming for large language models: beyond the few-shot paradigm. In: Extended abstracts of the 2021 CHI conference on human factors in computing systems, pp 1\u20137","DOI":"10.1145\/3411763.3451760"},{"issue":"11","key":"2880_CR35","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.14778\/3551793.3551841","volume":"15","author":"I Trummer","year":"2022","unstructured":"Trummer I (2022) Codexdb: synthesizing code for query processing from natural language instructions using GPT-3 codex. Proc VLDB Endow 15(11):2921\u20132928","journal-title":"Proc VLDB Endow"},{"key":"2880_CR36","unstructured":"White J, Fu Q, Hays S, Sandborn M, Olea C, Gilbert H, Elnashar A, Spencer-Smith J, Schmidt DC (2023) A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382"},{"issue":"1","key":"2880_CR37","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10916-023-01925-4","volume":"47","author":"M Cascella","year":"2023","unstructured":"Cascella M, Montomoli J, Bellini V, Bignami E (2023) Evaluating the feasibility of Chatgpt in healthcare: an analysis of multiple clinical and research scenarios. J Med Syst 47(1):33","journal-title":"J Med Syst"},{"issue":"6","key":"2880_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2023.100936","volume":"5","author":"G Levin","year":"2023","unstructured":"Levin G, Meyer R, Kadoch E, Brezinov Y (2023) Identifying chatgpt-written OBGYN abstracts using a simple tool. Am J Obstet Gynecol MFM 5(6):100936","journal-title":"Am J Obstet Gynecol MFM"},{"issue":"7","key":"2880_CR39","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1093\/asj\/sjad042","volume":"43","author":"R Gupta","year":"2023","unstructured":"Gupta R, Pande P, Herzog I, Weisberger J, Chao J, Chaiyasate K, Lee ES (2023) Application of chatgpt in cosmetic plastic surgery: ally or antagonist? Aesthetic Surg J 43(7):587\u2013590","journal-title":"Aesthetic Surg J"},{"issue":"1","key":"2880_CR40","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1038\/s41598-023-31412-2","volume":"13","author":"A Lahat","year":"2023","unstructured":"Lahat A, Shachar E, Avidan B, Shatz Z, Glicksberg BS, Klang E (2023) Evaluating the use of large language model in identifying top research questions in gastroenterology. Sci Rep 13(1):4164","journal-title":"Sci Rep"},{"key":"2880_CR41","doi-asserted-by":"crossref","unstructured":"Lyu Q, Tan J, Zapadka ME, Ponnatapura J, Niu C, Myers KJ, Wang G, Whitlow CT (2023) Translating radiology reports into plain language using chatgpt and gpt-4 with prompt learning: promising results, limitations, and potential. arXiv preprint arXiv:2303.09038","DOI":"10.1186\/s42492-023-00136-5"},{"key":"2880_CR42","doi-asserted-by":"publisher","DOI":"10.1126\/science.adg7879","volume-title":"ChatGPT is fun, but not an author","author":"HH Thorp","year":"2023","unstructured":"Thorp HH (2023) ChatGPT is fun, but not an author. American Association for the Advancement of Science, Washington"},{"issue":"7947","key":"2880_CR43","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/d41586-023-00288-7","volume":"614","author":"EA Van Dis","year":"2023","unstructured":"Van Dis EA, Bollen J, Zuidema W, Van Rooij R, Bockting CL (2023) Chatgpt: five priorities for research. Nature 614(7947):224\u2013226","journal-title":"Nature"},{"key":"2880_CR44","unstructured":"Germain M, Gregor K, Murray I, Larochelle H (2015) Made: masked autoencoder for distribution estimation. In: International Conference on Machine Learning, PMLR. pp 881\u2013889."},{"issue":"205","key":"2880_CR45","first-page":"1","volume":"17","author":"B Uria","year":"2016","unstructured":"Uria B, C\u00f4t\u00e9 M-A, Gregor K, Murray I, Larochelle H (2016) Neural autoregressive distribution estimation. J Mach Learn Res 17(205):1\u201337","journal-title":"J Mach Learn Res"},{"key":"2880_CR46","unstructured":"Fedus W, Goodfellow I, Dai AM (2018) Maskgan: better text generation via filling in the_. arXiv preprint arXiv:1801.07736"},{"key":"2880_CR47","doi-asserted-by":"crossref","unstructured":"Ouyang X, Zhou P, Li CH, Liu L (2015) Sentiment analysis using convolutional neural network. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing, pp. 2359\u20132364. IEEE","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.349"},{"key":"2880_CR48","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"2880_CR49","doi-asserted-by":"crossref","unstructured":"Li W (2020) Analysis of semantic comprehension algorithms of natural language based on robot\u2019s questions and answers. In: 2020 IEEE International conference on advances in electrical engineering and computer applications (AEECA), IEEE pp. 1021\u20131024.","DOI":"10.1109\/AEECA49918.2020.9213499"},{"issue":"8","key":"2880_CR50","first-page":"1359","volume":"15","author":"C Deguang","year":"2021","unstructured":"Deguang C, Jinlin M, Ziping M, Jie Z (2021) Review of pre-training techniques for natural language processing. J Front Comput Sci Tech 15(8):1359","journal-title":"J Front Comput Sci Tech"},{"key":"2880_CR51","unstructured":"Essays Corpus datasets (2023) https:\/\/www.kaggle.com\/competitions\/llm-detect-ai-generated-text\/data. Accessed 31 Oct 2023"},{"key":"2880_CR52","unstructured":"EnglishQA Essays Corpus datasets (2023) https:\/\/www.kaggle.com\/datasets\/thedrcat\/daigt-proper-train-dataset. Accessed 11 May 2023"},{"key":"2880_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asw.2024.100865","volume":"61","author":"SA Crossley","year":"2024","unstructured":"Crossley SA, Tian Y, Baffour P, Franklin A, Benner M, Boser U (2024) A large-scale corpus for assessing written argumentation: persuade 2.0. Assess Writ 61:100865","journal-title":"Assess Writ"},{"key":"2880_CR54","unstructured":"EnglishQA Essays Corpus datasets. https:\/\/www.kaggle.com\/datasets\/nbroad\/daigt-data-llama-70b-and-falcon180b. Accessed 11 May 2024"},{"issue":"1","key":"2880_CR55","first-page":"1","volume":"17","author":"HM Abbas","year":"2025","unstructured":"Abbas HM (2025) A novel approach to automated detection of ai-generated text. J Al-Qadisiyah Comput Sci Math 17(1):1\u201317","journal-title":"J Al-Qadisiyah Comput Sci Math"},{"key":"2880_CR56","unstructured":"EnglishQA Essays Corpus datasets (2024) https:\/\/www.kaggle.com\/datasets\/darraghdog\/hello-claude-1000-essays-from-anthropic. Accessed 11 May 2024"},{"key":"2880_CR57","unstructured":"EnglishQA Essays Corpus datasets (2023) (https:\/\/www.kaggle.com\/datasets\/alejopaullier\/daigt-external-dataset). Accessed 11 May 2023"},{"key":"2880_CR58","unstructured":"EnglishQA Essays Corpus datasets. https:\/\/www.kaggle.com\/datasets\/kingki19\/llm-generated-essay-using-palm-from-google-gen-ai. Accessed 11 May 2024"},{"key":"2880_CR59","unstructured":"EnglishQA Essays Corpus datasets. https:\/\/www.kaggle.com\/datasets\/radek1\/llm-generated-essays. Accessed 11 May 2024"},{"key":"2880_CR60","doi-asserted-by":"crossref","unstructured":"Naseem S, Mahmood T, Asif M, Rashid J, Umair M, Shah M (2021) Survey on sentiment analysis of user reviews. In: 2021 International Conference on Innovative Computing (ICIC), IEEE pp 1\u20136.","DOI":"10.1109\/ICIC53490.2021.9693029"},{"key":"2880_CR61","doi-asserted-by":"crossref","unstructured":"Chen A, Stanovsky G, Singh S, Gardner M (2019) Evaluating question answering evaluation. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp 119\u2013124","DOI":"10.18653\/v1\/D19-5817"},{"key":"2880_CR62","volume-title":"Statistical methods for speech recognition","author":"F Jelinek","year":"1998","unstructured":"Jelinek F (1998) Statistical methods for speech recognition. MIT press, Cambridge"},{"key":"2880_CR63","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th mof the association for computational linguistics, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"issue":"4","key":"2880_CR64","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427\u2013437","journal-title":"Inf Process Manag"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02880-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02880-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02880-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T09:56:01Z","timestamp":1773654961000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02880-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2880"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02880-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"22 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 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":"65"}}