From Divergent Guidelines to Delphi-Ready Items: Tutorial for Large Language Model-Assisted Guideline Harmonization Using Penile Cancer as a Worked Example.

Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.