{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T03:55:11Z","timestamp":1779940511878,"version":"3.53.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04285-x","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T14:18:55Z","timestamp":1765376335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-level fusion tokens for enhanced self-supervised skeleton-based action recognition"],"prefix":"10.1007","volume":"42","author":[{"given":"Jie","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaida","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjun","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"4285_CR1","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., Patel, N., Gholami, A., Nikkhah, M., Agrawal, M., Patel, V.M.: Adamae: adaptive masking for efficient spatiotemporal learning with masked autoencoders. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 14,507\u201314,517 (2023)","DOI":"10.1109\/CVPR52729.2023.01394"},{"key":"4285_CR2","unstructured":"Chen, Y., Zhang, Z., Yuan, C., Li, B., Deng, Y., Hu, W.: Channel-wise topology refinement graph convolution for skeleton-based action recognition. In: IEEE International Conference on Computer Vision, pp. 13,359\u201313,368 (2021)"},{"key":"4285_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhao, L., Yuan, J., Tian, Y., Xia, Z., Geng, S., Han, L., Metaxas, D.N.: Hierarchically self-supervised transformer for human skeleton representation learning. In: European Conference on Computer Vision, pp. 185\u2013202 (2022)","DOI":"10.1007\/978-3-031-19809-0_11"},{"key":"4285_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, Y.B., Chen, X., Chen, J., Wei, P., Zhang, D., Lin, L.: Hierarchical transformer: unsupervised representation learning for skeleton-based human action recognition. In: IEEE International Conference on Multimedia and Expo, pp. 1\u20136 (2021)","DOI":"10.1109\/ICME51207.2021.9428459"},{"key":"4285_CR5","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"4285_CR6","doi-asserted-by":"crossref","unstructured":"Dong, J., Sun, S., Liu, Z., Chen, S., Liu, B., Wang, X.: Hierarchical contrast for unsupervised skeleton-based action representation learning. In: AAAI Conference on Artificial Intelligence, pp. 525\u2013533 (2023)","DOI":"10.1609\/aaai.v37i1.25127"},{"key":"4285_CR7","doi-asserted-by":"crossref","unstructured":"Fan, C., Tian, J., Li, Y., Chen, W., He, H., Jin, Y.: Chain-of-thought tuning: masked language models can also think step by step in natural language understanding. In: Conference on Empirical Methods in Natural Language Processing, pp. 14,774\u201314,785 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.913"},{"key":"4285_CR8","unstructured":"Feichtenhofer, C., Fan, H., Li, Y., He, K.: Masked autoencoders as spatiotemporal learners. In: Advances in Neural Information Processing Systems (2022)"},{"key":"4285_CR9","doi-asserted-by":"crossref","unstructured":"Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In: AAAI Conference on Artificial Intelligence, pp. 762\u2013770 (2022)","DOI":"10.1609\/aaai.v36i1.19957"},{"key":"4285_CR10","unstructured":"Gupta, A., Wu, J., Deng, J., Li, F.F.: Siamese masked autoencoders. Adv. Neural Inf. Process. Syst.36, (2024)"},{"key":"4285_CR11","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.B.: Masked autoencoders are scalable vision learners. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 15,979\u201315,988 (2022)"},{"key":"4285_CR12","doi-asserted-by":"crossref","unstructured":"Hua, Y., Wu, W., Zheng, C., Lu, A., Liu, M., Chen, C., Wu, S.: Part aware contrastive learning for self-supervised action recognition. In: International Joint Conference on Artificial Intelligence, pp. 855\u2013863 (2023)","DOI":"10.24963\/ijcai.2023\/95"},{"key":"4285_CR13","doi-asserted-by":"crossref","unstructured":"Kim, B., Chang, H.J., Kim, J., Choi, J.Y.: Global-local motion transformer for unsupervised skeleton-based action learning. In: European Conference on Computer Vision, pp. 209\u2013225 (2022)","DOI":"10.1007\/978-3-031-19772-7_13"},{"key":"4285_CR14","unstructured":"Lai, C.I.: Contrastive predictive coding based feature for automatic speaker verification. arXiv preprint arXiv:1904.01575 (2019)"},{"key":"4285_CR15","unstructured":"Lee, J., Lee, M., Cho, S., Woo, S., Jang, S., Lee, S.: Leveraging spatio-temporal dependency for skeleton-based action recognition. In: IEEE International Conference on Computer Vision, pp. 10,221\u201310,230 (2023)"},{"key":"4285_CR16","doi-asserted-by":"crossref","unstructured":"Lin, L., Song, S., Yang, W., Liu, J.: MS2L: multi-task self-supervised learning for skeleton based action recognition. In: ACM International Conference on Multimedia, pp. 2490\u20132498 (2020)","DOI":"10.1145\/3394171.3413548"},{"key":"4285_CR17","doi-asserted-by":"crossref","unstructured":"Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2363\u20132372 (2023)","DOI":"10.1109\/CVPR52729.2023.00234"},{"key":"4285_CR18","doi-asserted-by":"crossref","unstructured":"Lincke, A., Fagerstr\u00f6m, C., Ekstedt, M., L\u00f6we, W., Back\u00e5berg, S.: A comparative study of the 2d- and 3d-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults. Health Informat. J. 29,(2023)","DOI":"10.1177\/14604582231214589"},{"key":"4285_CR19","doi-asserted-by":"crossref","unstructured":"Liu, C., Hu, Y., Li, Y., Song, S., Liu, J.: Pku-mmd: a large scale benchmark for continuous multi-modal human action understanding. arXiv preprint arXiv:1703.07475 (2017)","DOI":"10.1145\/3132734.3132739"},{"key":"4285_CR20","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","volume":"42","author":"J Liu","year":"2020","unstructured":"Liu, J., Shahroudy, A., Perez, M., Wang, G., Duan, L.Y., Kot, A.C.: Ntu rgb+d 120: a large-scale benchmark for 3d human activity understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2684\u20132701 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4285_CR21","doi-asserted-by":"crossref","unstructured":"Liu, M., Liu, H., Guo, T.: Cross-model cross-stream learning for self-supervised human action recognition. IEEE Trans. Hum.-Mach. Syst. 743\u2013752 (2024)","DOI":"10.1109\/THMS.2024.3467334"},{"key":"4285_CR22","doi-asserted-by":"crossref","unstructured":"Liu, M., Meng, F., Chen, C., Wu, S.: Novel motion patterns matter for practical skeleton-based action recognition. In: AAAI Conference on Artificial Intelligence, pp. 1701\u20131709 (2023)","DOI":"10.1609\/aaai.v37i2.25258"},{"key":"4285_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 143\u2013152 (2020)","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"4285_CR24","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)"},{"key":"4285_CR25","doi-asserted-by":"publisher","first-page":"8699","DOI":"10.1109\/TMM.2023.3239751","volume":"25","author":"C Pang","year":"2023","unstructured":"Pang, C., Lu, X., Lyu, L.: Skeleton-based action recognition through contrasting two-stream spatial-temporal networks. IEEE Trans. Multimedia 25, 8699\u20138711 (2023)","journal-title":"IEEE Trans. Multimedia"},{"key":"4285_CR26","unstructured":"Park, N., Kim, W., Heo, B., Kim, T., Yun, S.: What do self-supervised vision transformers learn? In: International Conference on Learning Representations (2023)"},{"key":"4285_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari, C., Cannici, M., Matteucci, M.: Skeleton-based action recognition via spatial and temporal transformer networks. Comput. Vis. Image Understand. 208, 103219 (2021)","journal-title":"Comput. Vis. Image Understand."},{"key":"4285_CR28","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1007\/s10055-023-00821-z","volume":"27","author":"JL Ponton","year":"2023","unstructured":"Ponton, J.L., Ceballos, V., Acosta, L., R\u00edos, A., Moncl\u00fas, E., Pelechano, N.: Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality. Virt. Real. 27, 2541\u20132560 (2023)","journal-title":"Virt. Real."},{"key":"4285_CR29","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s00371-019-01644-3","volume":"36","author":"Y Qin","year":"2020","unstructured":"Qin, Y., Mo, L., Li, C., Luo, J.: Skeleton-based action recognition by part-aware graph convolutional networks. Vis. Comput. 36, 621\u2013631 (2020)","journal-title":"Vis. Comput."},{"key":"4285_CR30","doi-asserted-by":"crossref","unstructured":"Qing, Z., Zhang, S., Huang, Z., Wang, X., Wang, Y., Lv, Y., Gao, C., Sang, N.: Mar: Masked autoencoders for efficient action recognition. IEEE Trans. Multimedia 26, 218\u2013233(2023)","DOI":"10.1109\/TMM.2023.3263288"},{"key":"4285_CR31","doi-asserted-by":"crossref","unstructured":"Qiu, H., Hou, B., Ren, B., Zhang, X.: Spatio-temporal tuples transformer for skeleton-based action recognition. arXiv preprint arXiv:2201.02849 (2022)","DOI":"10.1016\/j.neucom.2022.10.084"},{"key":"4285_CR32","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.neucom.2022.10.084","volume":"518","author":"H Qiu","year":"2023","unstructured":"Qiu, H., Hou, B., Ren, B., Zhang, X.: Spatio-temporal segments attention for skeleton-based action recognition. Neurocomputing 518, 30\u201338 (2023)","journal-title":"Neurocomputing"},{"key":"4285_CR33","unstructured":"Qu, H., Cai, Y., Liu, J.: Llms are good action recognizers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18,395\u201318,406 (2024)"},{"key":"4285_CR34","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2021.04.023","volume":"569","author":"H Rao","year":"2021","unstructured":"Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented skeleton based contrastive action learning with momentum lstm for unsupervised action recognition. Inf. Sci. 569, 90\u2013109 (2021)","journal-title":"Inf. Sci."},{"key":"4285_CR35","doi-asserted-by":"crossref","unstructured":"Shah, A., Roy, A., Shah, K., Mishra, S., Jacobs, D., Cherian, A., Chellappa, R.: Halp: Hallucinating latent positives for skeleton-based self-supervised learning of actions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 18,846\u201318,856 (2023)","DOI":"10.1109\/CVPR52729.2023.01807"},{"key":"4285_CR36","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: Ntu rgb+d: A large scale dataset for 3d human activity analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"4285_CR37","doi-asserted-by":"crossref","unstructured":"Shan, W., Liu, Z., Zhang, X., Wang, S., Ma, S., Gao, W.: P-stmo: pre-trained spatial temporal many-to-one model for 3d human pose estimation. In: European Conference on Computer Vision, pp. 461\u2013478 (2022)","DOI":"10.1007\/978-3-031-20065-6_27"},{"key":"4285_CR38","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Decoupled spatial-temporal attention network for skeleton-based action-gesture recognition. In: Asian Conference on Computer Vision, pp. 38\u201353 (2020)","DOI":"10.1007\/978-3-030-69541-5_3"},{"key":"4285_CR39","doi-asserted-by":"publisher","first-page":"5733","DOI":"10.1007\/s00371-023-03132-1","volume":"40","author":"S Sun","year":"2024","unstructured":"Sun, S., Jia, Z., Zhu, Y., Liu, G., Yu, Z.: Decoupled spatio-temporal grouping transformer for skeleton-based action recognition. Vis. Comput. 40, 5733\u20135745 (2024)","journal-title":"Vis. Comput."},{"key":"4285_CR40","doi-asserted-by":"crossref","unstructured":"Thoker, F.M., Doughty, H., Snoek, C.G.: Skeleton-contrastive 3d action representation learning. In: ACM International Conference on Multimedia, pp. 1655\u20131663 (2021)","DOI":"10.1145\/3474085.3475307"},{"key":"4285_CR41","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1109\/TMM.2023.3307933","volume":"26","author":"M Wang","year":"2024","unstructured":"Wang, M., Li, X., Chen, S., Zhang, X., Ma, L., Zhang, Y.: Learning representations by contrastive spatio-temporal clustering for skeleton-based action recognition. IEEE Trans. Multimedia 26, 3207\u20133220 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"4285_CR42","doi-asserted-by":"publisher","first-page":"6940","DOI":"10.1109\/TPAMI.2020.3032738","volume":"45","author":"M Wang","year":"2023","unstructured":"Wang, M., Ni, B., Yang, X.: Learning multi-view interactional skeleton graph for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45, 6940\u20136954 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4285_CR43","doi-asserted-by":"crossref","unstructured":"Wang, X., Dai, Y., Gao, L., Song, J.: Skeleton-based action recognition via adaptive cross-form learning. In: ACM International Conference on Multimedia, pp. 1670\u20131678 (2022)","DOI":"10.1145\/3503161.3547811"},{"key":"4285_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMM.2024.3405712","volume":"26","author":"X Wang","year":"2024","unstructured":"Wang, X., Mu, Y.: Localized linear temporal dynamics for self-supervised skeleton action recognition. IEEE Trans. Multimedia 26, 1\u201311 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"4285_CR45","doi-asserted-by":"crossref","unstructured":"Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C.: Masked feature prediction for self-supervised visual pre-training. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 14,668\u201314,678 (2022)","DOI":"10.1109\/CVPR52688.2022.01426"},{"key":"4285_CR46","doi-asserted-by":"crossref","unstructured":"Wen, Y.H., Gao, L., Fu, H., Zhang, F.L., Xia, S.: Graph cnns with motif and variable temporal block for skeleton-based action recognition. In: AAAI Conference on Artificial Intelligence, pp. 8989\u20138996 (2019)","DOI":"10.1609\/aaai.v33i01.33018989"},{"key":"4285_CR47","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, X.J., Kittler, J., Xu, T., Ahmed, S., Awais, M., Feng, Z.: Scd-net: Spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5949\u20135957 (2024)","DOI":"10.1609\/aaai.v38i6.28409"},{"key":"4285_CR48","doi-asserted-by":"crossref","unstructured":"Wu, W., Hua, Y., Zheng, C., Wu, S., Chen, C., Lu, A.: Skeletonmae: Spatial-temporal masked autoencoders for self-supervised skeleton action recognition. In: IEEE International Conference on Multimedia and Expo Workshops, pp. 224\u2013229 (2023)","DOI":"10.1109\/ICMEW59549.2023.00045"},{"key":"4285_CR49","doi-asserted-by":"crossref","unstructured":"Xie, J., Meng, Y., Zhao, Y., Nguyen, A., Yang, X., Zheng, Y.: Dynamic semantic-based spatial graph convolution network for skeleton-based human action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6225\u20136233 (2024)","DOI":"10.1609\/aaai.v38i6.28440"},{"key":"4285_CR50","doi-asserted-by":"crossref","unstructured":"Xu, B., Shu, X., Zhang, J., Dai, G., Song, Y.: Spatiotemporal decouple-and-squeeze contrastive learning for semisupervised skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst.35, 11,035\u201311,048 (2023)","DOI":"10.1109\/TNNLS.2023.3247103"},{"key":"4285_CR51","doi-asserted-by":"crossref","unstructured":"Xue, W., Yang, M., Liu, R., Takuma, Y., Takahiro, Y., Takeshi, K.: Spatial-temporal graph convolutional network for skeleton-based gait recognition. In: International Conference on Pattern Recognition and Machine Learning, pp. 77\u201382 (2022)","DOI":"10.1109\/PRML56267.2022.9882242"},{"key":"4285_CR52","doi-asserted-by":"crossref","unstructured":"Yan, H., Liu, Y., Wei, Y., Li, Z., Li, G., Lin, L.: Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training. In: IEEE International Conference on Computer Vision, pp. 5606\u20135618 (2023)","DOI":"10.1109\/ICCV51070.2023.00516"},{"key":"4285_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, H., Hou, Y., Zhang, W., Li, W.: Contrastive positive mining for unsupervised 3d action representation learning. In: European Conference on Computer Vision, pp. 36\u201351 (2022)","DOI":"10.1007\/978-3-031-19772-7_3"},{"key":"4285_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, L., Liu, J.: Hierarchical consistent contrastive learning for skeleton-based action recognition with growing augmentations. In: AAAI Conference on Artificial Intelligence, pp. 3427\u20133435 (2023)","DOI":"10.1609\/aaai.v37i3.25451"},{"key":"4285_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, L., Liu, J.: Prompted contrast with masked motion modeling: Towards versatile 3d action representation learning. In: ACM International Conference on Multimedia, pp. 7175\u20137183 (2023)","DOI":"10.1145\/3581783.3611774"},{"key":"4285_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, B., Li, W., Duan, L., Gan, C.: Stst: Spatial-temporal specialized transformer for skeleton-based action recognition. In: ACM International Conference on Multimedia, pp. 3229\u20133237 (2021)","DOI":"10.1145\/3474085.3475473"},{"key":"4285_CR57","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Duan, H., Rao, A., Su, B., Wang, J.: Self-supervised action representation learning from partial spatio-temporal skeleton sequences. In: AAAI Conference on Artificial Intelligence, pp. 3825\u20133833 (2023)","DOI":"10.1609\/aaai.v37i3.25495"},{"key":"4285_CR58","first-page":"1","volume":"41","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Shao, M., Sun, L., Xia, S.: Acl-sar: model agnostic adversarial contrastive learning for robust skeleton-based action recognition. Vis. Comput 41, 1\u201316 (2024)","journal-title":"Vis. Comput"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04285-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:03:20Z","timestamp":1772629400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04285-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4285"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04285-x","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"25 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","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":"37"}}