{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T08:38:00Z","timestamp":1792571880418,"version":"4.5.0"},"reference-count":101,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clinical practice guidelines are central instruments of evidence-based care; yet, in many domains, particularly rare or complex diseases, multiple guidelines coexist and frequently offer conflicting advice. Existing strategies for resolving such divergences are often labor-intensive, opaque, and insufficiently reproducible.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This tutorial aimed to develop and demonstrate a reproducible workflow for transforming divergent clinical guideline recommendations and literature-derived white areas into human-reviewed Delphi-ready candidate items.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following a step-by-step tutorial structure, we demonstrate the workflow using penile cancer as a worked example. The process comprises manual selection of clinical guidelines, a systematic PubMed search for literature-derived candidate issues potentially unaddressed by the selected guidelines, selection of 3 complementary large language models (LLMs), and execution of predefined prompts, multimodal recommendation extraction, semantic harmonization, and item-level guideline stance classification. The evaluative process combines a 3-LLM ensemble consistency check with an independent pre-Delphi appraisal by 4 penile cancer experts using a predefined 5-point suitability scale and acceptance threshold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the worked example in the field of penile cancer as a high-stakes use case, the workflow extracted 157 clinical recommendations from 5 guidelines and consolidated them into 68 harmonized guideline-derived items. Stance classification identified 15 uniformly supported items and 53 divergent, ambiguous, or incompletely addressed items. In parallel, literature processing generated 95 harmonized white-area items, of which 9 were classified as true white-area items not addressed by the included guidelines. The final methodological output was a set of 62 human-reviewed Delphi-ready candidate items for subsequent clinical consensus voting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This methodology provides a scalable, transparent, and transferable approach for identifying and structuring interguideline inconsistencies and evidence gaps. By combining LLM-assisted analysis with independent external expert appraisal, it supports the reproducible transformation of heterogeneous guideline content into Delphi-ready candidate items.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/96886","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T16:25:05Z","timestamp":1789057505000},"page":"e96886-e96886","source":"Crossref","is-referenced-by-count":0,"title":["From Divergent Guidelines to Delphi-Ready Items: Tutorial for Large Language Model\u2013Assisted Guideline Harmonization Using Penile Cancer as a Worked 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