ICF-Based Biopsychosocial Assessment With AI-Assisted Profile Prediction: Trapeziometacarpal Osteoarthritis Model

Trapeziometacarpal osteoarthritis (TMC OA) is a common condition affecting the base of the thumb that causes pain, weakness, and difficulty with daily hand use. Current clinical assessment often focuses on physical findings alone, without considering psychological and social factors that also influence patient outcomes. This study has three objectives organized as interrelated work packages: OBJECTIVE 1 (Clinical Assessment): To comprehensively assess individuals with TMC OA using the International Classification of Functioning, Disability and Health (ICF) framework. This includes evaluating pain, joint mobility, grip strength, daily activity limitations, social participation, psychological factors (anxiety, depression, fear of movement, pain beliefs), and environmental factors (family support, ergonomic adaptations). OBJECTIVE 2 (AI Knowledge Evaluation): To compare the medical knowledge performance of four large language models (Claude, ChatGPT, Gemini, LLaMA) in answering clinical questions about TMC OA, using criteria such as accuracy, reproducibility, comprehensiveness, clinical relevance, and readability. OBJECTIVE 3 (AI-Based Prediction): To analyze whether the best-performing large language model can predict multidimensional ICF-based patient profiles using only a limited set of core clinical parameters.

This research consists of three independent but interrelated work packages with different methods and targets. Work Package 1 (Clinical Data Collection and ICF-Based Profile Analysis): Participants with TMC OA will undergo a single face-to-face comprehensive assessment using a cross-sectional design. The assessment battery is structured according to the ICF framework and covers five domains: (a) Body Structure/Function: pain, joint mobility, grip and pinch strength, joint stability, and OA staging; (b) Activity: daily activity limitations and pain-activity patterns (avoidance, overdoing, pacing); (c) Participation: social, domestic, and occupational participation; (d) Personal Factors: pain beliefs, coping strategies, kinesiophobia, anxiety, and depression; (e) Environmental Factors: family support and ergonomic adaptations. Work Package 2 (Comparison of Large Language Models' Clinical Knowledge Performance): Four large language models (Claude, ChatGPT, Gemini, LLaMA) will be queried with questions frequently encountered in the TMC OA domain. Responses will be evaluated by subject matter experts using five criteria: accuracy, reproducibility (same questions repeated two times), comprehensiveness, clinical relevance, and readability (health literacy appropriateness). Work Package 3 (LLM-Based Predictive Profile Modeling): The best-performing LLM identified in WP2 will be provided with core clinical predictors from WP1 data. The model's predictions for multidimensional ICF-based patient profiles will be compared against actual assessment results using established agreement and performance metrics. Sample size: Based on a priori power analysis (alpha=0.05, power=0.80, effect size=0.131), a minimum of 93 participants is required.