{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T15:01:26Z","timestamp":1788015686639,"version":"build-2784847793"},"reference-count":5,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Generative artificial intelligence (AI) models offer potential assistance in pain research data acquisition, yet concerns persist regarding data accuracy and reliability. In a comparative study, we evaluated open generative AI models\u2019 capacity to acquire data on acute pain in rock climbers comparable to field research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty-two rock climbers (33\u2009m\/19\u2009f; age 29.0 [24.0\u201335.75] years) were asked to report pain location and intensity during a single climbing session. Five generative pretrained transformer models were tasked with responses to the same questions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Climbers identified the back of the forearm (19.2%) and toes (17.3%) as primary pain sites, with reported median pain intensity at 4 [3\u20135] and median maximum pain intensity at 7 [5\u20138]. Conversely, AI models yielded divergent findings, indicating fingers, hands, shoulders, legs, and feet as primary pain localizations with average and maximum pain intensity ranging from 3 to 4.4 and 5 to 10, respectively. Only two AI models provided references that were untraceable in PubMed and Google searches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings reveal that, currently, open generative AI models cannot match the quality of field-collected data on acute pain in rock climbers. Moreover, the models generated nonexistent references, raising concerns about their reliability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/sjpain-2023-0136","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:18:47Z","timestamp":1709810327000},"source":"Crossref","is-referenced-by-count":3,"title":["Could generative artificial intelligence replace fieldwork in pain research?"],"prefix":"10.1515","volume":"24","author":[{"given":"Suzana","family":"Bojic","sequence":"first","affiliation":[{"name":"Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11 000 Belgrade , Serbia"},{"name":"Department of Anesthesiology and Intensive Care, University Clinical Hospital Centre \u201cDr. Dragisa Misovic \u2013 Dedinje\u201d, Heroja Milana Tepica 1, 11 000 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nemanja","family":"Radovanovic","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Intensive Care, University Clinical Centre of Serbia, 11 000 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milica","family":"Radovic","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, University Clinical Hospital Center Zemun, 11 000 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dusica","family":"Stamenkovic","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Intensive Care, Medical Faculty, University of Defense, Veljka Lukica Kurjaka 1, 11 000 Belgrade , Serbia"},{"name":"Department of Anesthesiology and Intensive Care, Military Medical Academy, Crnotravska 17 , 11 000 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"2025120910063072710_j_sjpain-2023-0136_ref_001","doi-asserted-by":"crossref","unstructured":"Stokel-Walker C, Van Noorden R. ChatGPT and generative AI\u2019s impact on science. Nature. 2023;614(7947):214\u20136. 10.1038\/d41586-023-00340-6.","DOI":"10.1038\/d41586-023-00340-6"},{"key":"2025120910063072710_j_sjpain-2023-0136_ref_002","doi-asserted-by":"crossref","unstructured":"Schedlbauer J, Raptis G, Ludwig B. Web crawling, web scraping, and text mining for medical informatics labor market analysis. Int J Med Inform. 2021;150:104453. 10.1016\/j.ijmedinf.2021.104453.","DOI":"10.1016\/j.ijmedinf.2021.104453"},{"key":"2025120910063072710_j_sjpain-2023-0136_ref_003","doi-asserted-by":"crossref","unstructured":"Borji A. A categorical archive of ChatGPT failures. ArXiv; 2023. 10.48550\/arXiv.2302.03494.","DOI":"10.21203\/rs.3.rs-2895792\/v1"},{"key":"2025120910063072710_j_sjpain-2023-0136_ref_004","doi-asserted-by":"crossref","unstructured":"Spitale G, Biller-Andorno N, Germani F. GPT-3 AI model (dis)informs better than humans. Sci Adv. 2023;9(26):eadh1850. 10.1126\/sciadv.adh1850.","DOI":"10.1126\/sciadv.adh1850"},{"key":"2025120910063072710_j_sjpain-2023-0136_ref_005","doi-asserted-by":"crossref","unstructured":"Moskatel LS, Zhang N. An observational, qualitative study on the utility of ChatGPT in assessing literature on migraine prevention. Front Neurol. 2023;14:1225223. 10.3389\/fneur.2023.1225223.","DOI":"10.3389\/fneur.2023.1225223"}],"container-title":["Scandinavian Journal of Pain"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/sjpain-2023-0136\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/sjpain-2023-0136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T13:08:57Z","timestamp":1765285737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/sjpain-2023-0136\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":5,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,3,7]]},"published-print":{"date-parts":[[2024,3,7]]}},"alternative-id":["10.1515\/sjpain-2023-0136"],"URL":"https:\/\/doi.org\/10.1515\/sjpain-2023-0136","relation":{},"ISSN":["1877-8879"],"issn-type":[{"value":"1877-8879","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"20230136"}}