Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Background: It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia. Methods: This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment. Results: After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events. Conclusion The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.

Background: Denosumab (DMB) is a bone antiresorptive agent used to treat osteoporosis or metastatic cancer of the bones. However, denosumab-associated osteonecrosis of the jaw (DRONJ) has become a common complication in cancer patients. The prevalence of osteonecrosis of the jaw (ONJ) in cancer patients is estimated to be similar for both bisphosphonate-related cases (1.1 to 1.4%) and denosumab-related cases (0.8 to 2%), with the addition of adjunctive therapy with anti-angiogenic agents reportedly increasing its prevalence to 3%. (Spec Care Dentist 36(4):231–236, 2016). The aim of this study is to report on DRONJ in cancer patients treated with DMB (Xgeva ® , 120mg).Case presentation In this study, we identified four cases of ONJ among 74 patients receiving DMB therapy for meta‑ static cancer. Of the four patients, three had prostate cancer and one had breast cancer. Preceding tooth extraction within 2 months of the last DMB injection was found to be a risk factor for DRONJ. Pathological examination revealed that three patients had acute and chronic inflammation, including actinomycosis colonies. Among the four patients with DRONJ referred to us, three were successfully treated without complications and had no recurrence following surgical treatment, while one did not follow up. After healing, one patient experienced a recurrence at a different site.Sequestrectomy in conjunction with antibiotic therapy and cessation of DMB use proved to be effective in managing the condition, and the ONJ site healed after an average 5-month follow-up period. Conclusion Conservative surgery, along with antibiotic therapy and discontinuation of DMB, was found to be effec‑ tive in managing the condition. Additional studies are needed to investigate the contribution of steroids and antican‑ cer drugs to jaw bone necrosis, the prevalence of multicenter cases, and whether there is any drug interaction with DMB.

Objectives: The purpose of this study was to identify whether stress experienced by those working in the local civil service was related to their oral health during the COVID-19 pandemic. Methods: A survey was conducted on 431 civil servants from eight districts, currently working in the Daegu City Hall had COVID-19 related work duties during the pandemic. Results: Several factors associated with oral health related quality of life were explored. Demographic details revealed that men had significantly better oral health related life quality as compared to women; further, being younger, being unmarried, and having a lower position had better outcomes for oral health related quality of life. Regarding the relationship between oral health behavior and oral health related quality of life, it was found that the better the subjective oral health, the higher the rate of not visiting the dentist in the past year. The COVID-19 pandemic has been a particularly important time to explore in order to understand how the stress experienced by local government officials is related to their oral health. It has been especially noted that the higher the work stress, the worse the oral health related quality of life amongst individuals. Conclusions Results of this study emphasize that at a time when fatigue among civil servants is increasing due to the prolonged COVID-19 pandemic, oral conditions caused by stress should be identified and greater awareness should be created about oral health care.

Background/Aims: The clinical benefit of strict blood glucose-lowering therapy for patients with coronary artery disease (CAD) is still debated. We aimed to evaluate the long-term outcomes of patients with diabetes who underwent percutaneous coronary intervention (PCI), according to the mean hemoglobin A1c (HbA1c) level after PCI. Methods: We evaluated 675 diabetes patients with CAD treated with PCI. We categorized the study population into three groups based on the mean observed HbA1c levels during the follow-up duration, as follows: aggressive control (AC) group (HbA1c level < 6.5%, n = 148), moderate control (MC) group (HbA1c level ≥ 6.5% and < 7.0%, n = 138), and uncontrolled (UC) group (HbA1c level ≥ 7.0%, n = 389). The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCEs), defined as cardiac death, myocardial infarction, repeat target vessel revascularization, and stroke. Results: The mean HbA1c level of the AC group was significantly lower than that of the MC and UC groups (6.04% ± 0.36% vs. 6.74% ± 0.14% vs. 8.39% ± 1.20%, p < 0.001). The incidence of MACCEs was significantly lower in the AC group than in the MC and UC groups (16.0% vs. 24.3% vs. 26.3%, p = 0.010), mostly driven by the incidence of stroke (4.4% vs. 14.0% vs. 11.4%, p = 0.013). Multivariate Cox regression analysis showed that only the AC group was associated with a reduced rate of MACCEs (hazard ratio, 0.499; 95% confidence interval, 0.316 to 0.786; p = 0.004) compared with the UC group. Conclusions Our study showed that intensive glycemic control (HbA1c level < 6.5%) is associated with improved clinical outcomes after PCI in patients with diabetes.

Background and Objectives: The canalith reposition procedure (CRP) is used for the treatment of benign paroxysmal positional vertigo (BPPV) where the accuracy of position may affect the therapeutic efficacy. We investigate the accuracy of head position in CRP and its influencing factors during the procedure by measuring the position using inertial sensors and three dimensional remodeling.Subjects and Method We included 28 patients who were diagnosed as BPPV. To evaluate the accuracy of the CRP, we used the inertial sensor on the patient’s goggle used for videonystagmography. We evaluated the accuracy of the treatment compared to the textual treatment used during CRP. We also evaluated patient factors that affected the accuracy of head position as well as analyzing the correlation between the error rate and the successful treatment rate. Results: While the average error rate was 12.6±5.8% for the PSCC group, it was 10.2±5.2% for the lateral semicircular canal (LSCC) group. For the posterior semicircular canal (PSCC) the group with body mass index (BMI), less than 25 patients had the lower error rate than the group with BMI greater than 25. There was no significant differences regarding the error rate according to BMI or age in the PSCC group. There is no significant differences regarding the error rate between those treated within 1 week and those over 1 week. For the LSCC delayed treatment group, there was no significant differences of error rate between the 1st and 2nd maneuver at each position. Conclusion For the Epley maneuver, the error rate of patients with high BMI is higher than those with low BMI. When the repeated barbeque maneuver was conducted, patients could have a more accurate position due to the learning effect. Care should be taken to ensure accurate CRP by considering various factors.