{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T06:27:50Z","timestamp":1786343270784,"version":"build-2736575974"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"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>Background<\/jats:title>\n                    <jats:p>Immunotherapy plays an important role in bladder cancer care, requiring ongoing patient education, symptom monitoring, and communication of pathology- and biomarker-related information. Locally deployed large language models (LLMs) may support these nurse-led activities, but their safety and clinical usability remain uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We conducted a human-in-the-loop evaluation of a locally deployed Qwen2-7B-Instruct model using structured Mandarin-language benchmarks. Five task categories were assessed: general immunotherapy education, pathology-informed communication, biomarker and precision pathology communication, symptom triage, and pathology-risk-informed triage. Outputs were reviewed by specialist nurses, with physician or pathologist adjudication for high-risk, discordant, or pathology-related cases. Evaluation dimensions included accuracy, completeness, readability, actionability, safety, pathology-feature recognition, uncertainty communication, red-flag recognition, escalation appropriateness, and revision burden.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The locally deployed model Qwen2-7B achieved an overall score of 3.83 \u00b1 0.23 for general patient education, performing best in disease understanding and self-management but less well in warning-sign education. In pathology-informed communication, the model achieved an overall score of 3.57 \u00b1 0.22, with stronger performance in patient-friendly translation and structured pathology-feature recognition than in risk communication and follow-up guidance. Biomarker communication represented a more challenging task (overall score 3.26 \u00b1 0.25), with frequent needs for uncertainty statements, clinician referral, and correction of biomarker overinterpretation. In symptom triage, agreement with expert triage levels was 75.9%, red-flag recognition was 71.1%, and escalation sensitivity was 77.1%; under-triage and unsafe reassurance remained observable, particularly in high-risk irAE scenarios. Incorporating pathology-derived risk context improved triage agreement from 52.5 to 70.0% and reduced under-triage, although safety-critical errors persisted. Across all tasks, only 3.7% of outputs required no revision, whereas 31.4% required major revision or withholding. The highest review burden occurred in biomarker communication and high-risk irAE scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A locally deployed Qwen2-7B-based LLM can support nurse-reviewed communication in bladder cancer immunotherapy, particularly for standardized education and explanation of structured pathology information. However, biomarker communication, pathology-derived risk interpretation, and immune-related symptom escalation remain safety-sensitive tasks requiring structured nurse oversight and clinician backup. These findings support locally deployed LLMs as human-in-the-loop nursing support tools rather than autonomous communication or triage systems within precision uro-oncology care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1903690","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T05:30:18Z","timestamp":1786339818000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A locally deployed large language model for pathology-informed and nurse-reviewed communication support in bladder cancer immunotherapy"],"prefix":"10.3389","volume":"13","author":[{"given":"Suqing","family":"Diao","sequence":"first","affiliation":[{"name":"Department of Urology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University","place":["Huaian, 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