BACKGROUND: A novel technique for the repair of tears of the upper subscapularis tendon—intraarticular knotless fixation—has been introduced recently. The purpose of this study was to evaluate the clinical and structural outcomes of arthroscopic intraarticular knotless fixation for the treatment of upper subscapularis tendon tears. METHODS: We retrospectively analyzed the clinical and radiological outcomes of 27 patients who underwent arthroscopic intraarticular knotless fixation for upper subscapularis tendon tears. Finally, a total of 10 patients who could participate in at least a 6 month follow-up of magnetic resonance imaging evaluation and in a least 1-year follow-up on an outpatient basis were enrolled in our study. The mean age at the time of operation was 60.7 years, and the mean duration of follow-up was 14.7 months. Two patients had concomitant tears of the supraspinatus tendon and 8 patients had concomitant tears of the supraspinatus and the infraspinatus tendons. RESULTS: The clinical and radiological outcomes improved after the patients had undertaken arthroscopic intraarticular knotless fixation. The mean visual analogue scale score for pain during motion improved from 6.7 preoperatively to 1.4 at the final follow-up (p<0.001). The mean Constant score improved from 59.3 preoperatively to 79.6 at the final follow-up, and the mean the University of California at Los Angeles score, from 21.7 to 30.2, respectively (p<0.001 and p<0.001). The upper subscapularis tendon tear was healed in every patient except one (90%), for the patient had suffered from a postoperative trauma that resulted in a retear. CONCLUSIONS: We found that arthroscopic intraarticular knotless fixation gives good clinical and structural outcomes for the repair of upper subscapularis tendon tears. Arthroscopic intraarticular knotless fixation provided such a reliable and efficient restoration of the subscapularis tendon footprint that we anticipate it will become a widely-used procedure for upper subscapularis tendon tears.
Arthroscopy ; California ; Follow-Up Studies ; Humans ; Magnetic Resonance Imaging ; Outpatients ; Retrospective Studies ; Shoulder ; Tears* ; Tendons* ; Treatment Outcome

STATEMENT OF PROBLEM: In spite of increasing dental treatment for the disabled, there was no collected data on prosthodontic treatment status and dentists' perceptions in Korea. PURPOSE: The purpose of this study was to research the dentists' perceptions about current prosthodontic treatment status for the disabled in Korea and to suggest public health policies for the disabled. MATERIAL AND METHODS: Total of 68 dentists who have regularly treated the disabled were asked to fill questionnaires regarding prosthodontic treatment for the disabled. RESULTS: 1. 45.6% of dentists felt the necessity of an esthetic prosthesis for the disabled. However, 54.3% of dentists reported the improvement of access to implant,orthodontic and esthetic treatment is needed. 2. Most (79.4%) of the dentists thought it is imperative for the government to support the disabled to maintain adequate oral health by providing at least 50% of charge in their prosthesis. CONCLUSION: Through this research, 97.1% of dentists answered that the current prosthodontic treatment status for the disabled in Korea is poor. This indicated the necessity of the financial support from the government for the disabled.
Dentists ; Fees and Charges ; Financial Support ; Humans ; Korea ; Oral Health ; Prostheses and Implants ; Prosthodontics ; Public Health ; Surveys and Questionnaires

Background: Cryolipolysis, a preferred method for minimally invasive body contouring, involves the noninvasive cooling of adipocytes to induce lipolysis without damaging other tissues. This study aimed to evaluate the safety and efficacy of cryolipolysis for the treatment of excessive fat tissue. Methods: Between May 2014 and December 2017, 231 patients with 448 areas of interest were enrolled and their records were retrospectively reviewed. We used five different vacuum applicators, and the best-fitting applicator was used for each area. One cycle of cryolipolysis was applied at a cooling intensity factor of 41.6. The efficacy was evaluated 12 weeks after treatment via review of clinical photographs, the pinch test, and ultrasonographic measurements of fat thickness. The occurrence of any complications was also assessed. Results: The volume was reduced in all of the areas to which cryolipolysis was applied. The rate of reduction of the fat layer as measured via the pinch test was 19.2%, and the rate of the decrease in fat layer thickness as measured via ultrasonography was 22.8%. Fat reduction of the upper arm differed significantly from that of the abdomen and flank, but no significant difference was found between sexes. The side effects were limited to erythema, edema, bruising, and numbness at the treatment site and resolved without treatment. Conclusions Cryolipolysis, with new and better-fitting applicators, is safe, fast, and effective for the reduction of excessive fat tissue on the abdomen, back, flank, and extremities. It is a good option for treating excess adipose tissue in Asian patients.

BACKGROUND: Vascular endothelial dysfunction (VED) plays a pivotal role in the pathogenesis of atherosclerosis and is associated with insulin resistance and with visceral obesity. Therefore, in this study the predicting factor of vascular endothelial dysfunction was investigated in healthy premenopausal obese women by pulse-wave analysis (PWA) combined with provocative pharmacological testing. METHODS: Thirty three obese women (BMI> or =25), aged 20~45 y and 25 age-matched control subjects (BMI; 18.5~22.9) were examined. All women were sedentary (<1 hr/wk of physical activity), non-smoker and were excluded if they had type 2 diabetes melitus, hypertension, hyperlipidemia, cardiovascular disease, or acute inflammatory disease and were studied in folicullar phase of the cycle, within the first week after cessation of menstrual bleeding. They underwent determination of anthropometric measurements, metabolic variables, adipose tissue regional distribution, and endothelial function by performing pulse-wave analysis (PWA) combined with provocative pharmacological testing. RESULTS: Augmentation Index (AIx) fell significantly after the administration of salbutamol, which causes endothelium-dependent vasodilatation, but response was significantly reduced in obese women compared with controls (10.28 6.72% vs 17.2 6.84%, P=0.0003). The change in after Nitroglycerin, which causes endothelium-independent vasodilatation, did not differ significantly (30.86 9.67% vs 30.6 10.11%, P=0.9172). In our obese subjects, visceral adipose tissue area was independently a significant predictor of vascular endothelial dysfunction (beta= 0.1381, P=0.0038, Adj-R2=0.348). CONCLUSION: Increased abdominal adiposity is a powerful independent predictor of VED in obese healthy women. Future studies of vascular endothelial function should account for the independent effects of abdominal fat.
Abdominal Fat ; Adipose Tissue ; Adiposity ; Albuterol ; Atherosclerosis ; Cardiovascular Diseases ; Female ; Hemorrhage ; Humans ; Hyperlipidemias ; Hypertension ; Insulin Resistance ; Intra-Abdominal Fat ; Nitroglycerin ; Obesity, Abdominal ; Vasodilation

BACKGROUND: This study was intended to look into the characteristics and problems when FACES III are used as tools to evaluate family function of adolescents. METHODS: From May to June 1996, self-reported questionnaires for FACES III were conducted on 2,430 middle school and high school students (males:1,190, females:1,240) in Seoul and Yangpyung, Kyunggi-Do. There were eight schools (four middle schools, four high schools) in Seoul and two schools (one middle school, one high school) in Yangpyung, Kyunggi-Do in this study. Also, educational level of parents, occupational status of parents, family types (extended/nuclear), number of family member and numbers of siblings were assessed. RESULTS: The mean values of adaptability were significantly higher in Seoul students, in females, and in nuclear families. The mean values were higher in single sibling than two or more and in parents with higher education. The mean values of cohesion were significantly higher in Seoul students, in middle school students and in family members of 5 and under. The mean values were higher in those without a sibling and in those with above college education. In stepwise regression analysis, educational level of parents, gender, area, occupational status of mother and grade were associated significantly with the mean values of adaptability and educational level of parents, grade and numbers of siblings were significantly associated with the mean values of cohesion. CONCLUSIONS: We consider that there are problems in deciding the cut-off point and in the questionnaires of survey when the FACES III is used as a tool for evaluating family function of adolescents. Trials of combining other tools of family function or consideration of lower concepts are needed in other to be applicable to the individual conversation and the clinical surroundings.
Adolescent* ; Education ; Employment ; Female ; Gyeonggi-do ; Humans ; Mothers ; Nuclear Family ; Parents ; Seoul ; Siblings ; Surveys and Questionnaires

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.