Makale detayı · 2025 · article
Is Readability a Proxy for Reliability? A Qualitative Evaluation of ChatGPT‐4.0 in Orthopaedic Trauma Communication
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- YÖKSİSYÖKSİS makale kaydı
- YÖKSİS dergi adıJournal of Evaluation in Clinical Practice
- Katalog eşleşmesi (ISSN)Journal of Evaluation in Clinical Practice
- OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)
- Semantic Scholaratıf sayısı (OpenAlex ile birleştirilmez)
Özet
AIM: This study aimed to evaluate the accuracy, readability, and safety of ChatGPT-4.0's responses to frequently asked questions (FAQs) related to orthopaedic trauma and to examine whether readability is associated with the quality and reliability of content. METHODS: Ten common patient questions related to orthopaedic emergencies were submitted to ChatGPT-4.0. Each response was assessed independently by three orthopaedic trauma surgeons using a 4-point ordinal scale for accuracy, clinical appropriateness, and safety. Readability was calculated using the Flesch-Kincaid Grade Level (FKGL). Inter-rater agreement was analysed using intraclass correlation coefficients (ICC). The presence of disclaimers was also recorded. RESULTS: ChatGPT-4.0's responses had a mean FKGL score of 10.5, indicating high school-level readability. Stratified analysis showed comparable readability scores across response quality categories: excellent (10.0), poor (9.8), and dangerous (10.1), suggesting that readability does not predict content reliability. Accuracy and safety scores varied considerably among responses, with the highest inter-rater agreement in clinical appropriateness (ICC = 0.81) and the lowest in safety assessments (ICC = 0.68). Notably, nine out of 10 responses included a disclaimer indicating the nonprofessional nature of the content, with one omission observed in a high-risk clinical scenario. CONCLUSION: Although ChatGPT-4.0 provides generally readable responses to orthopaedic trauma questions, readability does not reliably distinguish between accurate and potentially harmful information. These findings highlight the need for expert review when using AI-generated content in clinical communication.
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