{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T04:20:09Z","timestamp":1782879609333,"version":"3.54.5"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["41691078"],"award-info":[{"award-number":["41691078"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Traditional methods in handwritten text recognition primarily focus on generating basic transcriptions, which often fall short for in-depth humanities research. Our study enhances this by providing diplomatic transcriptions for German studies, meticulously reproducing the original manuscripts, including layout and expanded abbreviations. State-of-the-art sequence-to-sequence approaches for handwritten text recognition predominantly use Connectionist Temporal Classification\u00a0(CTC) as an auxiliary loss of the encoder output to improve robustness and accuracy. This is not possible in this task due to the great differences in the length of diplomatic transcriptions. We propose using the basic transcription instead of the diplomatic one as an additional target for the CTC feedback. Additionally, we introduce positional encoding at the intersection between the encoder and decoder to resolve the conflict of competing encoder objectives, balancing CTC loss reduction with the maintenance of implicit positional encoding for the decoder. Our empirical tests on the newly created dataset \u201cNuremberg Letterbooks\u201d demonstrate significant data efficiency improvements. With only 4000 training lines (about 130 transcribed pages), we achieve a Character Error Rate\u00a0(CER) of 9.39% without expanded abbreviations and 12.07% with expanded abbreviations, outperforming the baseline errors of 14.26% and 68.21%, respectively.<\/jats:p>","DOI":"10.1007\/s11042-024-20545-9","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T05:28:45Z","timestamp":1735882125000},"page":"39107-39122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data-efficient handwritten text recognition of diplomatic historical text"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3706-285X","authenticated-orcid":false,"given":"Martin","family":"Mayr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharina","family":"Neumeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Krenz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"B\u00fcrcky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1240-5809","authenticated-orcid":false,"given":"Florian","family":"Kordon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9153-1031","authenticated-orcid":false,"given":"Mathias","family":"Seuret","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3889-6629","authenticated-orcid":false,"given":"Jochen","family":"Z\u00f6llner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4196-0289","authenticated-orcid":false,"given":"Fei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-5284","authenticated-orcid":false,"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0455-3799","authenticated-orcid":false,"given":"Vincent","family":"Christlein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"20545_CR1","doi-asserted-by":"publisher","unstructured":"Wegera K (2007) Die Entstehung der Neuhochdeutschen Schriftsprache, 2nd edn. Dokumentation Germanistischer Forschung, vol 7. Peter Lang, Frankfurt\/Main. https:\/\/doi.org\/10.3726\/978-3-653-05147-6","DOI":"10.3726\/978-3-653-05147-6"},{"key":"20545_CR2","doi-asserted-by":"publisher","unstructured":"Graves A, Fern\u00e1ndez S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on machine learning pp 369\u2013376. https:\/\/doi.org\/10.1145\/1143844.1143891","DOI":"10.1145\/1143844.1143891"},{"issue":"8","key":"20545_CR3","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"20545_CR4","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (Eds) Advances in neural information processing systems, vol 30"},{"key":"20545_CR5","doi-asserted-by":"publisher","unstructured":"Puigcerver J (2017) Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR) vol 01, pp 67\u201372. https:\/\/doi.org\/10.1109\/ICDAR.2017.20","DOI":"10.1109\/ICDAR.2017.20"},{"key":"20545_CR6","doi-asserted-by":"publisher","unstructured":"Li M, Lv T, Chen J, Cui L, Lu Y, Florencio D, Zhang C, Li Z, Wei F (2023) Trocr: Transformer-based optical character recognition with pre-trained models. In: Proceedings of the AAAI conference on artificial intelligence vol 37, no 11, pp 13094\u201313102. https:\/\/doi.org\/10.1609\/aaai.v37i11.26538","DOI":"10.1609\/aaai.v37i11.26538"},{"key":"20545_CR7","doi-asserted-by":"publisher","unstructured":"Str\u00f6bel PB, Hodel T, Boente W, Volk M (2023) The adaptability of a transformer-based ocr model for historical documents. In: Coustaty M, Forn\u00e9s A (Eds) Document analysis and recognition \u2013 ICDAR 2023 Workshops, Springer, Cham pp 34\u201348. https:\/\/doi.org\/10.1007\/978-3-031-41498-5_3","DOI":"10.1007\/978-3-031-41498-5_3"},{"key":"20545_CR8","unstructured":"Bachlechner T, Majumder BP, Mao H, Cottrell G, McAuley J (2021) ReZero is all you need: fast convergence at large depth. In: Campos C, Maathuis MH (Eds) Proceedings of the thirty-seventh conference on uncertainty in artificial intelligence. Proceedings of Machine Learning Research, vol 161, pp 1352\u20131361. https:\/\/proceedings.mlr.press\/v161\/bachlechner21a.html"},{"key":"20545_CR9","doi-asserted-by":"publisher","unstructured":"Tensmeyer C, Wigington C (2019) Training full-page handwritten text recognition models without annotated line breaks. In: 2019 International conference on document analysis and recognition (ICDAR), pp 1\u20138. https:\/\/doi.org\/10.1109\/ICDAR.2019.00011","DOI":"10.1109\/ICDAR.2019.00011"},{"key":"20545_CR10","doi-asserted-by":"publisher","unstructured":"Coquenet D, Chatelain C, Paquet T (2021) Span: a simple predict & align network for handwritten paragraph recognition. In: Llad\u00f3s J, Lopresti D, Uchida S (Eds) Document analysis and recognition \u2013 ICDAR 2021, Springer, Cham pp 70\u201384. https:\/\/doi.org\/10.1007\/978-3-030-86334-0_5","DOI":"10.1007\/978-3-030-86334-0_5"},{"key":"20545_CR11","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.patrec.2021.11.010","volume":"155","author":"AC Rouhou","year":"2022","unstructured":"Rouhou AC, Dhiaf M, Kessentini Y, Salem SB (2022) Transformer-based approach for joint handwriting and named entity recognition in historical document. Pattern Recognit Lett 155:128\u2013134. https:\/\/doi.org\/10.1016\/j.patrec.2021.11.010","journal-title":"Pattern Recognit Lett"},{"key":"20545_CR12","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.patrec.2023.03.020","volume":"169","author":"M Dhiaf","year":"2023","unstructured":"Dhiaf M, Rouhou AC, Kessentini Y, Salem SB (2023) Msdoctr-lite: a lite transformer for full page multi-script handwriting recognition. Pattern Recognit Lett 169:28\u201334. https:\/\/doi.org\/10.1016\/j.patrec.2023.03.020","journal-title":"Pattern Recognit Lett"},{"issue":"7","key":"20545_CR13","doi-asserted-by":"publisher","first-page":"8227","DOI":"10.1109\/TPAMI.2023.3235826","volume":"45","author":"D Coquenet","year":"2023","unstructured":"Coquenet D, Chatelain C, Paquet T (2023) Dan: a segmentation-free document attention network for handwritten document recognition. IEEE Trans Pattern Anal Mach Intell 45(7):8227\u20138243. https:\/\/doi.org\/10.1109\/TPAMI.2023.3235826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20545_CR14","unstructured":"Graves A, Schmidhuber J (2008) Offline handwriting recognition with multidimensional recurrent neural networks. In: Koller D, Schuurmans D, Bengio Y, Bottou L (Eds) Advances in neural information processing systems, vol 21"},{"key":"20545_CR15","doi-asserted-by":"publisher","unstructured":"Pham V, Bluche T, Kermorvant C, Louradour J (2014) Dropout improves recurrent neural networks for handwriting recognition. In: 2014 14th International conference on frontiers in handwriting recognition pp 285\u2013290. https:\/\/doi.org\/10.1109\/ICFHR.2014.55","DOI":"10.1109\/ICFHR.2014.55"},{"key":"20545_CR16","doi-asserted-by":"publisher","unstructured":"Bluche T, Messina R (2017) Gated convolutional recurrent neural networks for multilingual handwriting recognition. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 01, pp 646\u2013651. https:\/\/doi.org\/10.1109\/ICDAR.2017.111","DOI":"10.1109\/ICDAR.2017.111"},{"key":"20545_CR17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s10032-022-00401-y","volume":"25","author":"S Cascianelli","year":"2022","unstructured":"Cascianelli S, Cornia M, Baraldi L, Cucchiara R (2022) Boosting modern and historical handwritten text recognition with deformable convolutions. Int J Doc Anal Recognit (IJDAR) 25:207\u2013217. https:\/\/doi.org\/10.1007\/s10032-022-00401-y","journal-title":"Int J Doc Anal Recognit (IJDAR)"},{"key":"20545_CR18","doi-asserted-by":"publisher","unstructured":"Coquenet D, Chatelain C, Paquet T (2022) End-to-end handwritten paragraph text recognition using a vertical attention network. IEEE Trans Pattern Anal Mach Intell 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2022.3144899","DOI":"10.1109\/TPAMI.2022.3144899"},{"key":"20545_CR19","unstructured":"Diaz DH, Qin S, Ingle R, Fujii Y, Bissacco A (2021) Rethinking text line recognition models. arXiv:2104.07787"},{"key":"20545_CR20","doi-asserted-by":"publisher","first-page":"107482","DOI":"10.1016\/j.patcog.2020.107482","volume":"108","author":"M Yousef","year":"2020","unstructured":"Yousef M, Hussain KF, Mohammed US (2020) Accurate, data-efficient, unconstrained text recognition with convolutional neural networks. Pattern Recognit 108:107482. https:\/\/doi.org\/10.1016\/j.patcog.2020.107482","journal-title":"Pattern Recognit"},{"key":"20545_CR21","doi-asserted-by":"publisher","unstructured":"Quir\u00f3s L, Bosch V, Serrano L, Toselli AH, Vidal E (2018) From hmms to rnns: computer-assisted transcription of a handwritten notarial records collection. In: 2018 16th International conference on frontiers in handwriting recognition (ICFHR) pp 116\u2013121. https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00029","DOI":"10.1109\/ICFHR-2018.2018.00029"},{"key":"20545_CR22","doi-asserted-by":"publisher","unstructured":"Romero V, Toselli AH, Vidal E, S\u00e1nchez JA, Alonso C, Marqu\u00e9s L (2019) Modern vs diplomatic transcripts for historical handwritten text recognition. In: Cristani M, Prati A, Lanz O, Messelodi S, Sebe N (Eds) New trends in image analysis and processing \u2013 ICIAP 2019, Springer, Cham pp 103\u2013114. https:\/\/doi.org\/10.1007\/978-3-030-30754-7_11","DOI":"10.1007\/978-3-030-30754-7_11"},{"key":"20545_CR23","doi-asserted-by":"publisher","unstructured":"Michael J, Labahn R, Gr\u00fcning T, Z\u00f6llner J (2019) Evaluating sequence-to-sequence models for handwritten text recognition. In: 2019 International conference on document analysis and recognition (ICDAR) pp 1286\u20131293. https:\/\/doi.org\/10.1109\/ICDAR.2019.00208","DOI":"10.1109\/ICDAR.2019.00208"},{"key":"20545_CR24","unstructured":"Chowdhury A, Vig L (2018) An efficient end-to-end neural model for handwritten text recognition. In: British machine vision conference (BMVC)"},{"key":"20545_CR25","doi-asserted-by":"publisher","first-page":"108766","DOI":"10.1016\/j.patcog.2022.108766","volume":"129","author":"L Kang","year":"2022","unstructured":"Kang L, Riba P, Rusi\u00f1ol M, Forn\u00e9s A, Villegas M (2022) Pay attention to what you read: non-recurrent handwritten text-line recognition. Pattern Recognit 129:108766. https:\/\/doi.org\/10.1016\/j.patcog.2022.108766","journal-title":"Pattern Recognit"},{"key":"20545_CR26","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"20545_CR27","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2020) RoBERTa: a robustly optimized BERT pretraining approach. https:\/\/openreview.net\/forum?id=SyxS0T4tvS"},{"key":"20545_CR28","doi-asserted-by":"publisher","unstructured":"Wick C, Z\u00f6llner J, Gr\u00fcning T (2021) Transformer for handwritten text recognition using bidirectional post-decoding. In: Llad\u00f3s J, Lopresti D, Uchida S (Eds) Document analysis and recognition \u2013 ICDAR 2021, Springer, Cham pp 112\u2013126. https:\/\/doi.org\/10.1007\/978-3-030-86334-0_8","DOI":"10.1007\/978-3-030-86334-0_8"},{"key":"20545_CR29","doi-asserted-by":"publisher","unstructured":"Wick C, Z\u00f6llner J, Gr\u00fcning T (2022) Rescoring sequence-to-sequence models for text line recognition with ctc-prefixes. In: Uchida S, Barney E, Eglin V (Eds) Document analysis systems, Springer, Cham pp 260\u2013274. https:\/\/doi.org\/10.1007\/978-3-031-06555-2_18","DOI":"10.1007\/978-3-031-06555-2_18"},{"issue":"8","key":"20545_CR30","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/JSTSP.2017.2763455","volume":"11","author":"S Watanabe","year":"2017","unstructured":"Watanabe S, Hori T, Kim S, Hershey JR, Hayashi T (2017) Hybrid CTC\/attention architecture for end-to-end speech recognition. IEEE J Sel Top Signal Process 11(8):1240\u20131253. https:\/\/doi.org\/10.1109\/JSTSP.2017.2763455","journal-title":"IEEE J Sel Top Signal Process"},{"key":"20545_CR31","doi-asserted-by":"publisher","unstructured":"Barrere K, Soullard Y, Lemaitre A, Co\u00fcasnon B (2022) A light transformer-based architecture for handwritten text recognition. In: Uchida S, Barney E, Eglin V (Eds) Document analysis systems, Springer, Cham pp 275\u2013290. https:\/\/doi.org\/10.1007\/978-3-031-06555-2_19","DOI":"10.1007\/978-3-031-06555-2_19"},{"issue":"1","key":"20545_CR32","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"U-V Marti","year":"2002","unstructured":"Marti U-V, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recognit 5(1):39\u201346. https:\/\/doi.org\/10.1007\/s100320200071","journal-title":"Int J Doc Anal Recognit"},{"key":"20545_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-023-00459-2","author":"K Barrere","year":"2024","unstructured":"Barrere K, Soullard Y, Lemaitre A, Co\u00fcasnon B (2024) Training transformer architectures on few annotated data: an application to historical handwritten text recognition. Int J Doc Anal Recognit (IJDAR). https:\/\/doi.org\/10.1007\/s10032-023-00459-2","journal-title":"Int J Doc Anal Recognit (IJDAR)"},{"key":"20545_CR34","doi-asserted-by":"crossref","unstructured":"Mayr M, Krenz J, Neumeier K, Bub A, B\u00fcrcky S, Brolich N, Herbers K, Habermann M, Fleischmann P, Maier A, Christlein V (2024) Nuremberg letterbooks: a multi-transcriptional dataset of early 15th century manuscripts for document analysis. arXiv:2411.07138","DOI":"10.1038\/s41597-025-05144-z"},{"key":"20545_CR35","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"20545_CR36","doi-asserted-by":"publisher","unstructured":"Retsinas G, Sfikas G, Gatos B, Nikou C (2022) Best practices for a handwritten text recognition system. In: Uchida S, Barney E, Eglin V (Eds) Document analysis systems, Springer, Cham pp 247\u2013259. https:\/\/doi.org\/10.1007\/978-3-031-06555-2_17","DOI":"10.1007\/978-3-031-06555-2_17"},{"key":"20545_CR37","doi-asserted-by":"publisher","unstructured":"Grosicki E, El-Abed H (2011) ICDAR 2011 - french handwriting recognition competition. In: 2011 International conference on document analysis and recognition, pp 1459\u20131463. https:\/\/doi.org\/10.1109\/ICDAR.2011.290","DOI":"10.1109\/ICDAR.2011.290"},{"key":"20545_CR38","unstructured":"Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J (2020) On the variance of the adaptive learning rate and beyond. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=rkgz2aEKDr"},{"key":"20545_CR39","unstructured":"Izmailov P, Podoprikhin D, Garipov T, Vetrov D, Wilson A (2018) Averaging weights leads to wider optima and better generalization. In: Silva R, Globerson A, Globerson A (Eds) 34th Conference on uncertainty in artificial intelligence 2018, UAI 2018. 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pp 876\u2013885"},{"key":"20545_CR40","doi-asserted-by":"publisher","unstructured":"Tarride S, Boillet M, Kermorvant C (2023) Key-value information extraction from full handwritten pages. In: Fink GA, Jain R, Kise K, Zanibbi R (Eds) Document analysis and recognition - ICDAR 2023, Springer, Cham pp 185\u2013204. https:\/\/doi.org\/10.1007\/978-3-031-41679-8_11","DOI":"10.1007\/978-3-031-41679-8_11"},{"key":"20545_CR41","unstructured":"Wei H, Liu C, Chen J, Wang J, Kong L, Xu Y, Ge Z, Zhao L, Sun J, Peng Y, Han C, Zhang X (2024) General OCR theory: towards OCR-2.0 via a unified end-to-end model. arXiv:2409.01704"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20545-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20545-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20545-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T15:12:16Z","timestamp":1758899536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20545-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":41,"journal-issue":{"issue":"32","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["20545"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20545-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"assertion":[{"value":"17 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","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 authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}