Background: The proliferation of artificial intelligence has led to widespread patient use of large language models (LLMs). Purpose : We sought to characterize LLM responses to questions about piriformis syndrome (PS). Methods : On August 15, 2024, we asked 3 LLMs—ChatGPT-4, Copilot, and Gemini—to respond to the 25 most frequently asked questions about PS, as tracked by Google Trends. We evaluated the accuracy and completeness of the responses according to the Likert scale. We used the Ensuring Quality Information for Patients (EQIP) tool to assess the quality of the responses and assessed readability using Flesch–Kincaid Reading Ease (FKRE) and Flesch–Kincaid Grade Level (FKGL) scores. Results : The mean completeness scores of the responses obtained from ChatGPT, Copilot, and Gemini were 2.8 ± 0.3, 2.2 ± 0.6, and 2.6 ± 0.4, respectively. There was a significant difference in the mean completeness score among LLMs. In pairwise comparisons, ChatGPT and Gemini were superior to Copilot. There was no significant difference between the LLMs in terms of mean accuracy scores. In readability analyses, no significant difference was found in terms of FKRE scores. However, a significant difference was found in FKGL scores. A significant difference between LLMs was identified in the quality analysis performed according to EQIP scores. Conclusion : Although the use of LLMs in healthcare is promising, our findings suggest that these technologies need to be improved to perform better in terms of accuracy, completeness, quality, and readability on PS for a general audience.

The Oswestry Disability Index is considered the gold standard in the evaluation of disability in patients with chronic mechanical back pain. The aim of this study was to assess the applicability of Oswestry Disability Index in patients with ankylosing spondylitis and its relationship with disease assessment parameters for ankylosing spondylitis.

PuRPOse Of THe sTuDy In this study, we aimed to evaluate acromiohumeral distance (AHD) and supraspinatus tendon (sT) thickness measurements and their relationship with pain and function in sT pathologies. MATeRIAl AND MeTHODs The study included 111 patients and 25 healthy controls (HC). Patients were divided into 3 groups according to their diagnosis: non-tear tendinopathy (NTT), partial thickness tear (PTT), and full thickness tear (fTT). The AHD and sT thickness of the participants were measured with ultrasound. The pain and functional status of the patients were evaluated with the Numeric Rating scale (NRs), The QuickDAsH shortened version of the DAsH Outcome Measure-Disabilities of the Arm shoulder and Hand (QDAsH), and simple shoulder Test (ssT). ResulTs The AHD value was significantly higher in the NTT group (p=0.000). The AHD value was significantly lower in the fTT group (p=0.000). sT thickness value was significantly lower in the PTT group compared to the NTT group (p=0.000). There was a positive correlation between sT thickness and BMI (r=0.553,p<0.01). There was a negative correlation between sT thickness and ssT and a positive correlation between sT thickness (r=-0.223,p<0.05) and QDAsH (r=0.276,p<0.05). CONCLUSiONS We found that AHD and ssT thicknesses significantly differed in the NTT, PTT, fTT, and HC groups. This difference may be important for diagnosis. In addition, the effect of obesity on sT thickness and the relationship between sT thickness and functional scores may be considered. Weight control may be effective at this point.