Aslihan Uzun Bektas

Objectives: With advances in genetic testing, monogenic lupus is increasingly recognized, yet disease-specific classification criteria are lacking. We compared all three classification criteria sets in the same monogenic lupus cohort and, for the first time, evaluated their performance across distinct genetic pathways. Methods: This national multicentre study included 88 genetically confirmed monogenic lupus patients from 22 paediatric rheumatology centres across Türkiye. Fulfilment of the AmericanCollege of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012 and European League Against Rheumatism (EULAR)/ACR-2019 criteria and pathway-based differences in classification performance were assessed. Results: Overall, 74% (n = 65) fulfilled ACR-1997, 70.5% (n = 62) SLICC-2012 and EULAR/ACR-2019 criteria, and 62.5% (n = 55) met all three, whereas 17% (n = 15) met none. Agreement was substantial (κ = 0.63–0.73), with no significant differences between criteria (P > 0.05). Classification performance differed significantly across genetic pathways (P < 0.05), with the highest fulfilment performances in complement deficiencies and the lowest in nucleic acid metabolism and type I interferon pathway defects. Most patients who did not fulfil any classification criteria had nucleic acid metabolism and clearance defects (13/15, 86.7%). Domain-level analyses showed that classification success was mainly driven by mucocutaneous, immunologic and renal domains. Complement and immune-regulation defects showed a more classical lupus phenotype, with most fulfilling all three criteria (78.6%, 22/28 and 77.8%, 14/18), whereas the proportion was lower in type I interferon pathway and nucleic acid metabolism and clearance defects (33.3%, 2/6 and 47.2%, 17/36). No classification criteria were superior within genetic pathways. Conclusion: One-third of genetically confirmed monogenic lupus patients remained unclassified, particularly those with interferon-related and nucleic acid metabolism defects, highlighting pathway-dependent limitations of current classification criteria.

Familial Mediterranean fever (FMF) is the most common monogenic autoinflammatory disease. Large language models (LLMs) offer rapid access to medical information. This study evaluated and compared the reliability, quality, and accuracy of ChatGPT-4o and LLaMA-3.1 for FMF. Single Hub and Access Point for Paediatric Rheumatology in Europe (SHARE) and European League Against Rheumatism (EULAR) guidelines were used for question generation and also for answer validation. Thirty-one questions were developed from a clinician’s perspective based on the related guidelines. Two pediatric rheumatologists with over 20 years of FMF experience independently and blindly evaluated the responses. Reliability, quality, and accuracy were assessed using the modified DISCERN Scale, Global Quality Score, and the guidelines, respectively. Readability was assessed using multiple established indices. Statistical analyses included the Shapiro–Wilk test to assess normality, followed by paired t-tests for normally distributed scores, and Wilcoxon signed-rank tests for non-normally distributed scores. Both models demonstrated moderate reliability and high response quality. In terms of alignment with the guidelines, LLaMA provided fully aligned, complete, and accurate responses to 51.6% (16/31) of the questions, whereas ChatGPT provided such responses to 80.6% (25/31). While 9.7% (4/31) of LLaMA’s responses were entirely contradictory to the guidelines, ChatGPT did not produce any such responses. ChatGPT outperformed LLaMA in terms of accuracy, quality, and reliability, with statistical significance. Readability assessments showed that both LLMs required college-level understanding. Conclusion: While LLMs show great promise, current limitations in accuracy and guideline adherence mean they should supplement, not replace, clinical expertise. Clinical trial registration: This study does not involve clinical trials; therefore, no clinical trial registration is required. What is Known: •FMF is the most common hereditary autoinflammatory disease. LLMs are increasingly used to provide clinical information. What is New: •This is the first study to assess two different LLMs in the context of FMF, evaluating their reliability, quality, and alignment with clinical guidelines. ChatGPT-4o outperformed LLaMA-3.1 in reliability,quality, and guideline alignment for FMF. However, both models showed informational gaps that may limit their clinical use.

Background. Osteogenesis imperfecta (OI) is a genetic disorder of connective tissues caused by an abnormality in the synthesis or processing of type I collagen. The combination of OI and inflammatory arthritis is rare. Our literature review identified 5 cases of OI-related inflammatory arthritis, but only 2 of these cases have been reported in children. Case Report. We present 3 cases diagnosed with OI and juvenile idiopathic arthritis (JIA). Two were diagnosed with enthesitis-associated arthritis, and one was diagnosed with oligoarticular JIA with laboratory findings and a magnetic resonance imaging examination. Only one of the patients had a previously diagnosed OI. For the others, whole gene sequence analysis was performed, and a mutation in the collagen type I alpha 1 (COL1A1) gene was detected. Identifying and treating inflammatory arthritis in our patients with OI improved their joint pain. Conclusion. Musculoskeletal pain is a common issue in individuals with OI and JIA. Considering children with OI may also develop arthritis, early diagnosis, and accurate treatment may be crucial. Recognizing the rare association between JIA and OI is important, as investigating this relationship could help alleviate the disease burden. Thorough evaluation and prompt diagnosis of JIA in patients with OI can significantly reduce the impact of the disease.