This survey was conducted from January 1991 to May 1991. Two hundred and eight residents in 4 government supported homes for the aged and hundred and eleven living in the community in Taegu, Korea, were examined to evaluate the depression and the anxiety of the aged with combined anxiety and depression scale (CADS) and somatization symptom check list (SCL). There are no significant differences between residents in the home for the aged and those living in the community on the total scores of CADS and SCL. However, in the case of the total score of CADS of the female subjects in the home for the aged were significantly higher than those of the community residents. The elderly in the home for the aged tended to have pure depression, while community residents were likely to have anxiety and depression. Fifty-two subjects of home for the aged and sixty-nine of the community scored over 50 points of CADS, which indicates considerable depression or anxiety. In psychosocial factors, the subjects who in the following situations had statistically significant higher scores than others. The results were as follow. Poor health, unhappiness, unsatisfaction to the past occupation, pessimistic thought in future view for both group and unsatisfaction of the relationship with familiar people for the elderly in the community. The SCL scores of two groups subgrouped by under 49 and 50 on CADS showed significant differences between each subgroup on all of the SCL items. It could be suggested that somatic symptoms for the aged is a sign of depression.
Aged* ; Anxiety* ; Daegu ; Depression* ; Female ; Homes for the Aged ; Humans ; Korea ; Occupations ; Psychology

No abstract available.
Autopsy* ; Statistics as Topic*

PURPOSE: Recently, vancomycin-resistant enterococci (VRE) have become one of the major nosocomial pathogens in Korea. However, there have been few studies on the epidemiology of VRE colonization among neonates. In this study, we investigated the prevalence of VRE colonization, risk factors for VRE, and how to control the spread of VRE infection in the Neonatal Intensive Care Unit (NICU) of Pusan National University Hospital (PNUH). METHODS: We retrospectively reviewed medical records of 192 neonates who were admitted to the NICU of PNUH from March 2006 to March 2007. Surveillance cultures from rectal swabs for detecting VRE were obtained weekly during the study period. We analyzed the prevalence of VRE and various risk factors. RESULTS: The rate of VRE colonization among NICU patients was 25% (48/192). Thirty five of these VRE colonized patients were transferred to the NICU from other local hospitals. Compared with the non-VRE group, the risk factors associated with VRE colonization were lower birth weight, congenital heart disease, applied mechanical ventilation, use of a central venous catheter, chest tubing, a history of surgery, and use of antibiotics. CONCLUSION: VRE colonization among patients admitted to the NICU is rapidly increasing. Monitoring and managing premature neonates from the beginning of the birth process, avoiding many invasive procedures, avoiding antibiotics such as vancomycin and third generation cephalosporin are important for preventing the emergence and spread of VRE colonization in the NICU.
Anti-Bacterial Agents ; Birth Weight ; Central Venous Catheters ; Colon ; Heart Diseases ; Humans ; Infant ; Infant, Newborn ; Intensive Care, Neonatal ; Korea ; Medical Records ; Parturition ; Prevalence ; Respiration, Artificial ; Retrospective Studies ; Risk Factors ; Thorax ; Vancomycin ; Vancomycin Resistance

PURPOSE: The goal of this study was to evaluate the diagnostic accuracy of preoperative fine needle aspiration biopsy cytology (FNAB-C) in predicting lateral lymph node metastasis in papillary thyroid cancer patients. METHODS: A total of 592 patients who underwent thyroid cancer surgery and intra-operative lateral cervical LN frozen section or RND, from January 2002 to December 2011, were evaluated retrospectively. Among them, 228 cases had suspicious findings in FNAB-C of lateral nodes. We reviewed their radiological and pathological reports. RESULTS: Intra-operative frozen section examination was performed in 540 cases and RND was performed in 314 cases. This study included 534 women (83.4%) and 106 men (16.6%). Patients' ranged in age from 9 to 83 years (mean, 45.65 years). FNAB-C was performed in 228 cases. The sensitivity and specificity of FNAB-C was 71.5% and 78.6%, respectively; 35.9% of cases had a false negative result. The combination of FNAB-C and intra-operative frozen section test sensitivity and specificity was 87.2% and 93.6%, respectively. CONCLUSION: The results for sensitivity in FNAB-C actually appear low, and false negative results were very high. In papillary thyroid cancer in patients with FNAB-C, even if the result is negative, if lymph node metastasis is suspected based on radiologic evidence, frozen section examination should be performed for determination of metastasis.
Biopsy ; Biopsy, Fine-Needle ; Female ; Frozen Sections ; Humans ; Lymph Nodes ; Male ; Neoplasm Metastasis* ; Retrospective Studies ; Sensitivity and Specificity ; Thyroid Neoplasms*

No abstract available.

No abstract available.
Dermatitis, Exfoliative ; Phototherapy ; Psoriasis

No abstract available.
Electromyography* ; Hand* ; Humans ; Muscle Spasticity* ; Stroke*

Objectives: Assessing medical disputes requires both medical and legal expertise, presenting challenges for patients seeking clarity regarding potential malpractice claims. This study aimed to develop and evaluate a chatbot based on a chain-of-thought pipeline using a large language model (LLM) for providing medical dispute counseling and compare its performance with responses from human experts. Methods: Retrospective counseling cases (n = 279) were collected from the Korea Medical Dispute Mediation and Arbitration Agency’s website, from which 50 cases were randomly selected as a validation dataset. The Claude 3.5 Sonnet model processed each counseling request through a five-step chain-of-thought pipeline. Thirty-eight experts evaluated the chatbot’s responses against the original human expert responses, rating them across four dimensions on a 5-point Likert scale. Statistical analyses were conducted using Wilcoxon signed-rank tests. Results: The chatbot significantly outperformed human experts in quality of information (p < 0.001), understanding and reasoning (p < 0.001), and overall satisfaction (p < 0.001). It also demonstrated a stronger tendency to produce opinion-driven content (p < 0.001). Despite generally high scores, evaluators noted specific instances where the chatbot encountered difficulties. Conclusions A chain-of-thought–based LLM chatbot shows promise for enhancing the quality of medical dispute counseling, outperforming human experts across key evaluation metrics. Future research should address inaccuracies resulting from legal and contextual variability, investigate patient acceptance, and further refine the chatbot’s performance in domain-specific applications.

Objectives: Assessing medical disputes requires both medical and legal expertise, presenting challenges for patients seeking clarity regarding potential malpractice claims. This study aimed to develop and evaluate a chatbot based on a chain-of-thought pipeline using a large language model (LLM) for providing medical dispute counseling and compare its performance with responses from human experts. Methods: Retrospective counseling cases (n = 279) were collected from the Korea Medical Dispute Mediation and Arbitration Agency’s website, from which 50 cases were randomly selected as a validation dataset. The Claude 3.5 Sonnet model processed each counseling request through a five-step chain-of-thought pipeline. Thirty-eight experts evaluated the chatbot’s responses against the original human expert responses, rating them across four dimensions on a 5-point Likert scale. Statistical analyses were conducted using Wilcoxon signed-rank tests. Results: The chatbot significantly outperformed human experts in quality of information (p < 0.001), understanding and reasoning (p < 0.001), and overall satisfaction (p < 0.001). It also demonstrated a stronger tendency to produce opinion-driven content (p < 0.001). Despite generally high scores, evaluators noted specific instances where the chatbot encountered difficulties. Conclusions A chain-of-thought–based LLM chatbot shows promise for enhancing the quality of medical dispute counseling, outperforming human experts across key evaluation metrics. Future research should address inaccuracies resulting from legal and contextual variability, investigate patient acceptance, and further refine the chatbot’s performance in domain-specific applications.

Objectives: Assessing medical disputes requires both medical and legal expertise, presenting challenges for patients seeking clarity regarding potential malpractice claims. This study aimed to develop and evaluate a chatbot based on a chain-of-thought pipeline using a large language model (LLM) for providing medical dispute counseling and compare its performance with responses from human experts. Methods: Retrospective counseling cases (n = 279) were collected from the Korea Medical Dispute Mediation and Arbitration Agency’s website, from which 50 cases were randomly selected as a validation dataset. The Claude 3.5 Sonnet model processed each counseling request through a five-step chain-of-thought pipeline. Thirty-eight experts evaluated the chatbot’s responses against the original human expert responses, rating them across four dimensions on a 5-point Likert scale. Statistical analyses were conducted using Wilcoxon signed-rank tests. Results: The chatbot significantly outperformed human experts in quality of information (p < 0.001), understanding and reasoning (p < 0.001), and overall satisfaction (p < 0.001). It also demonstrated a stronger tendency to produce opinion-driven content (p < 0.001). Despite generally high scores, evaluators noted specific instances where the chatbot encountered difficulties. Conclusions A chain-of-thought–based LLM chatbot shows promise for enhancing the quality of medical dispute counseling, outperforming human experts across key evaluation metrics. Future research should address inaccuracies resulting from legal and contextual variability, investigate patient acceptance, and further refine the chatbot’s performance in domain-specific applications.