{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:14:58Z","timestamp":1788498898534,"version":"build-2803163510"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Bladder cancer often presents with hematuria, while modern management increasingly depends on pathology-based risk stratification, immunotherapy, and biomarker-informed treatment. This study evaluated whether social media videos adequately connect early hematuria warning with precision bladder cancer care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a cross-sectional, AI-assisted structured extraction and urologist consensus-scored content analysis of publicly available videos from Bilibili, Douyin, and YouTube. Videos were searched on May 17, 2026, using \u201c\u8840\u5c3f\u201d for the hematuria module and \u201c\u8180\u80f1\u764c\u201d for the bladder cancer module, with relevance ranking. For each module, one predefined keyword was used for video retrieval to maintain a consistent and reproducible search strategy across platforms. DISCERN, GQS, PEMAT, general content completeness, the Bladder Cancer Warning Score (BCWS), and the Bladder Cancer Precision Immunotherapy Score (BC-PIS) were assessed. After rubric calibration, the three reviewer-level BC-PIS scores were averaged per video for the primary quantitative analysis, and videos with a mean BC-PIS \u226511 were classified as adequate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the hematuria module, 368 of 400 videos were included. BCWS differed significantly across platforms, with mean scores of 6.59\u2009\u00b1\u20091.57 for Bilibili, 7.34\u2009\u00b1\u20091.09 for Douyin, and 7.99\u2009\u00b1\u20091.65 for YouTube (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Adequate warning information was present in 48\/120, 79\/99, and 120\/149 videos, respectively. In the bladder cancer module, 363 of 395 videos were included. The three-rater mean BC-PIS differed significantly across platforms: 3.28\u2009\u00b1\u20093.45 for Bilibili, 10.21\u2009\u00b1\u20092.03 for Douyin, and 7.71\u2009\u00b1\u20094.75 for YouTube (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Adequate precision immunotherapy information was identified in 7\/124, 33\/90, and 44\/149 videos, respectively. Reliability analysis yielded overall ICC(2,1) values of 0.693 for BCWS and 0.876 for BC-PIS; corresponding Fleiss' kappa values were 0.691 and 0.725. The calibrated Douyin reassessment improved BC-PIS reproducibility to an ICC(2,1) of 0.783 (95% CI, 0.653\u20130.864) and a Fleiss' kappa of 0.598 (95% CI, 0.459\u20130.718). All 149 included YouTube hematuria videos were matched by platform-specific sequence number and retained in the BCWS reliability analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Social media videos incompletely connect hematuria warning with pathology-based precision immunotherapy in bladder cancer. Greater professional involvement and platform-level quality control are needed to improve online bladder cancer education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1910134","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:24:10Z","timestamp":1788495850000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From hematuria warning to precision immunotherapy: an AI-assisted cross-platform analysis of bladder cancer information on social media"],"prefix":"10.3389","volume":"13","author":[{"given":"Peng","family":"Han","sequence":"first","affiliation":[{"name":"Department of Urology, The First Affiliated Hospital with Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangsu Key Laboratory 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonglei","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangsu Key Laboratory of Urological Disease Prevention and Treatment, The Second Affiliated Hospital of Nanjing Medical University, Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeping","family":"Gui","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangsu Key Laboratory of Urological Disease Prevention and Treatment, The Second Affiliated Hospital of Nanjing Medical University, Nanjing Medical University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiping","family":"Zong","sequence":"additional","affiliation":[{"name":"Department of Urology, The Affiliated Yixing Hospital of Jiangsu University","place":["Yixing, Jiangsu, 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