Cognitive apprenticeship and reflective practice are fundamental educational theories supporting postgraduate clinical training. Community hospital rotations provide ideal opportunities to apply these theories. However, community hospitals face challenges in securing educational time due to faculty shortages and heavy clinical workloads, leading to on-the-job training becoming the primary educational approach. Consequently, opportunities for structured instruction and reflection may be limited, potentially hindering the implementation of cognitive apprenticeship and reflective practice. To address this mismatch between educational needs and available resources, we implemented a remote educational conference focused on clinical questions (CQs) arising from residents' clinical experiences. Unlike traditional clinical conferences that focus on determining patient management, this initiative centers on reflective dialogue based on CQs formulated by residents themselves. By integrating experiential learning theory and reflective practice theory and focusing specifically on the latter three steps of cognitive apprenticeship, we successfully constructed an effective educational model for remote learning environments. This practice enables high-quality medical education that transcends geographical constraints and is considered valuable for future community-based medical education.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

This case involves a man in his 60s who was undergoing rituximab therapy and developed COVID-19 pneumonia and showed persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Despite treatments with dexamethasone, remdesivir, tocilizumab, and other drugs, the patient remained SARS-CoV-2 positive and ultimately died of respiratory failure. Patients receiving B-cell depletion therapy have an increased risk of persistent viral infections due to immunosuppression, underscoring the importance of early intervention with antiviral drugs and neutralizing antibodies in such cases.