ArticleBMC medical research methodology2025
AI-powered classification and network analysis for knowledge mapping in medicine: a century of neurosyphilis research.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTracking the evolution of scientific knowledge is challenging due to the scale and complexity of the biomedical literature. Neurosyphilis is a clinically complex and historically stigmatized condition that remains difficult to diagnose and manage. Its underexplored literature offers an ideal test case to evaluate digital methods for mapping research trends and identifying knowledge gaps. We aim to assess how large language models (LLMs), network analysis, and interrupted time series analysis (ITSA) can be combined to automate literature classification and examine how knowledge of neurosyphilis has evolved.
methodsWe systematically searched Web of Science, Embase, PubMed Central, the Cochrane Library, and Lens for records on neurosyphilis published until December 31, 2024. We included records with available titles and abstracts in which GPT-4o mini was identified as being focused primarily on syphilis or neurosyphilis. Eligible records were classified into 23 research fields via LLM-based prompts. Network analysis visualized changes in research structures over time, and the ITSA assessed associations between publication trends and major clinical or technological milestones.
resultsAmong the 14 934 retrieved records, 4 646 met the inclusion criteria. LLM-based classification showed high repeatability (agreement = 99·67%, 95% CI 99·47–99·80; Cohen’s κ = 0·99, 95% CI 0·96–1·00). Biomedical, Clinical, and Health sciences were the most common domains. Network analysis revealed a shift from dense, discipline-specific clusters to larger interdisciplinary structures. ITSA revealed significant increases in publication activity following the introduction of penicillin G, HIV emergence, genome sequencing of Treponema pallidum, and the rise of digital dissemination platforms.
conclusionsCombining LLMs with bibliometric and network methods provides a scalable framework for analyzing large-scale biomedical literature. When applied to neurosyphilis, the approach revealed links between research activity and clinical and technological advances. In addition to this case study, the method could support meta-research and inform evidence-based decision-making across other complex medical conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.