The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: The purpose of this study was to compare the effectiveness of pericapsular nerve group (PENG) block with periarticular multimodal drug injection (PMDI) on postoperative pain management and surgical outcomes in patients who underwent total hip arthroplasty (THA). We hypothesized that PENG block with PMDI would exhibit superior effects on postoperative pain control after THA compared to PMDI alone. Materials and Methods: From April 2022 to February 2023, 58 patients who underwent THA were randomly assigned into two groups: PENG block with PMDI group (n=29) and PMDI-only group (n=29). Primary outcomes were postoperative numeric rating scale (NRS) at rest and during activity at 6, 24, and 48 hours postoperatively. Secondary outcomes were postoperative complications (nausea and vomiting), Richards-Campbell Sleep Questionnaire (RCSQ) score, length of hospital stay, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index, Harris Hip Score (HHS), and total morphine usage after surgery. Results: There was no significant difference in postoperative pain for either resting NRS or active NRS. Postoperative nausea and vomiting, RCSQ score, length of hospital stay, WOMAC index, HHS, and total morphine usage exhibited no significant differences between the two groups. Conclusion Both groups showed no significant differences in postoperative pain and clinical outcomes, indicating that the addition of PENG block to PMDI does not improve pain management after applying the posterolateral approach of THA. PMDI alone during THA would be an efficient, fast, and safe method for managing postoperative pain. This article was registered with ClinicalTrials.gov (Gov ID: NCT05320913).