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.
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Grip Strength
Timeframe: Baseline (single assessment at enrollment)
Pinch Strength
Timeframe: Baseline (single assessment at enrollment)
Thumb Opposition (Kapandji Score)
Timeframe: Baseline (single assessment at enrollment)
Pain Intensity
Timeframe: Baseline (single assessment at enrollment)
Pain Duration
Timeframe: Baseline (single assessment at enrollment)
Radiographic Severity (Eaton-Littler Stage)
Timeframe: Baseline (single assessment at enrollment)
Radial Subluxation Ratio
Timeframe: Baseline (single assessment at enrollment)
Upper Extremity Disability (QuickDASH)
Timeframe: Baseline (single assessment at enrollment)
Hand Disability (Turkish Thumb Disability Index - TDX)
Timeframe: Baseline (single assessment at enrollment)
Joint Hypermobility (Beighton Score)
Timeframe: Baseline (single assessment at enrollment)
Thumb Joint Range of Motion
Timeframe: Baseline (single assessment at enrollment)
Provocative Tests
Timeframe: Baseline (single assessment at enrollment)
Environmental Factors: Social Support and Ergonomic Adaptations
Timeframe: Baseline (single assessment at enrollment)
Emotional Status (Hospital Anxiety and Depression Scale)
Timeframe: Baseline (single assessment at enrollment)
Kinesiophobia Level (Tampa Scale of Kinesiophobia)
Timeframe: Baseline (single assessment at enrollment)
Pain-Activity Patterns (Patterns of Activity Measure-Pain).
Timeframe: Baseline (single assessment at enrollment).
Pain Beliefs Profile (Pain Beliefs Questionnaire)
Timeframe: Baseline (single assessment at enrollment).
Pain Coping Strategies (Pain Coping Questionnaire).
Timeframe: Baseline (single assessment at enrollment).
Large Language Model Clinical Knowledge Accuracy
Timeframe: Baseline (single assessment during the data collection period)
Large Language Model Response Reproducibility
Timeframe: Within 24 hours after initial query
Large Language Model Content Comprehensiveness
Timeframe: Baseline (single assessment at enrollment)
Large Language Model Clinical Relevance
Timeframe: Baseline (single assessment)
Large Language Model Readability Score
Timeframe: Baseline (calculated immediately after response generation)
LLM Prediction Accuracy for Continuous ICF Profiles
Timeframe: Within 3 months after the completion of clinical data collection.
LLM Prediction Accuracy for Categorical ICF Profiles
Timeframe: Within 3 months after the completion of clinical data collection.