Jyoban Hospital

Abstract Purpose: Patients with metastatic urothelial carcinoma (mUC) often experience impaired renal function because of tumor invasion, nephroureterectomy, and chemotherapy. Enfortumab vedotin (EV) has been approved in Japan as a third-line treatment for advanced UC. In this study, we aimed to assess renal function changes in patients with mUC receiving EV therapy. Methods: We retrospectively analyzed renal function changes and clinical outcomes in 63 patients with mUC who received EV following platinum-based chemotherapy and immune checkpoint inhibitors. Data were collected from five institutions in Japan from September 2021 to September 2024. Results: The median baseline estimated glomerular filtration rate (eGFR) was 44.1 mL/min/1.73 m². Chronic kidney disease stage remained unchanged in 54% of patients, upstaging in 17%, and downstaging in 29%. The median change rate in eGFR was 2.6%. The median overall survival was 12.0 months in the eGFR ≥ 45 mL/min/1.73 m² group and 15.4 months in the eGFR < 45 mL/min/1.73 m² group. The objective response rate was 45% in the eGFR ≥ 45 mL/min/1.73 m² group and 34% in the eGFR < 45 mL/min/1.73 m² group. Adverse events (AEs) of any grade occurred in 84% of patients with eGFR ≥ 45 and 69% of those with eGFR < 45, with grade ≥3 AEs reported in 7% and 13% patients, respectively. Conclusions: EV did not significantly impair renal function in patients with mUC. Comparable efficacy and safety were observed regardless of baseline eGFR, supporting the feasibility of EV in patients with impaired renal function.

Conflicts of interest (COIs) in medical research can introduce biases affecting research integrity. This study investigated the scale and disclosure of COIs among Japanese authors of cancer clinical trials. Fifty-eight cancer randomized controlled trials from 2020, involving 226 Japanese authors, were systematically abstracted and analyzed. The financial relationships between these authors and pharmaceutical companies were examined using payment data from 56 companies for the years 2018-2019. COI statements were extracted from each publication, and COI declaration rates were calculated by comparing declared companies against those that reported payments. The analysis was conducted at both the author and article levels. For author-level analyses, the declaration rate was calculated per declaration rather than per author. For article-level analyses, payments were aggregated and attributed to their respective articles. Phase III trials comprised 82.8% (48/58) of the included studies. Of 226 authors, 202 (89.4%) were medical doctors and 208 (92.0%) received at least one payment. The median number of companies providing at least one payment per author was 9 (IQR, 4-12), with a median total payment of $19,183 US dollars (USD; IQR, $5,158-$62,534 USD). Among 280 unique disclosures from authors with payments, only 29 (10.4%) accurately reported COIs, whereas 80 (28.6%) reported no COIs. For 58 articles, only two (3.4%) accurately reported COIs, seven (12.1%) reported no COIs, and 49 (84.5%) partially disclosed COI information. Japanese cancer clinical trial authors frequently received industry payments, yet their COI disclosures were often inadequate. This highlights the need for stricter guidelines and improved education on COI reporting in Japan.

Introduction and background Non-consensual sex, including rape and sexual assault, has been a global concern and may have been influenced by the COVID-19 pandemic. However, information on this topic is limited. Therefore, our objective was to survey the incidence rate of non-consensual sex among Japanese women aged 15-79 years between April and September 2020, during the early stages of the COVID-19 pandemic in Japan. Materials and methods We utilized data from a sample of approximately 2.2 million individuals who participated in a web-based self-reported questionnaire survey from a nationwide, cross-sectional internet survey conducted in Japan between August and September 2020. Sampling weights were applied to calculate national estimates, and multivariable logistic regression was performed to identify factors associated with non-consensual sex. Data were extracted from a web-based, self-administered survey of approximately 2.2 million individuals. Results The study examined the incidence of non-consensual sex among12,809 women with valid responses,finding an overall rate of 1.1% across all participants. Higher rates were observed among women aged 20-29 (2.4%) and employed women (1.5%) compared to unemployed women (0.7%). No significant difference was noted based on living areas. Increased FCV-19S scores, worsening or improving mental health before COVID-19, suicidal thoughts, and feelings of isolation were all linked to higher incidence rates. Non-payment of salary and lack of money for necessities also correlated with higher rates. Key risk factors included age 15-19 or 20-29, employment, financial instability, suicidal thoughts, and isolation. Notably, 20% of women aged 15-19 reported suicidal ideation. Conclusions This study underscores the critical need for mental and financial support for young women, highlighting the importance of early intervention for economically vulnerable groups. Comprehensive education on sexual consent is essential, especially during societal upheavals like the COVID-19 pandemic, to prevent non-consensual sex and support affected individuals.

Following the Great East Japan Earthquake (GEJE) and the Fukushima Daiichi nuclear power plant accident in 2011, mandatory evacuation orders were issued to residents living near the nuclear power plant in Fukushima prefecture, including some patients receiving home oxygen therapy. Although the vulnerability of patients with home oxygen therapy (one of the population groups most vulnerable to disasters) has been noted, there is little information on the health effects of evacuation in the event of a radiation disaster. A 90-year-old man diagnosed with chronic obstructive pulmonary disease since the age of 70, and lived in a town located approximately 20 km south of the nuclear power plant, died 8 months after the disaster due to worsening health conditions. This case reveals the potential for both physical and psychological burdens experienced by vulnerable groups like patients undergoing home oxygen therapy during evacuations in times of disaster. Although it is only a case report and the information is limited, severe respiratory distress requiring home oxygen therapy may present a significant risk factor for disaster-related deaths, especially in cases where evacuations are prolonged, such as in nuclear disasters. Due to the challenge of obtaining prompt public support immediately after a disaster, home oxygen therapy patients may need to prioritize self-help and mutual assistance in their disaster preparedness efforts.

Abstract After the Great East Japan Earthquake, planning the appropriate allocation of healthcare resources is crucial. However, accurately estimating the medical care demand was challenging due to the substantial population fluctuations caused by extensive evacuations. This study employs mobile spatial statistics using NTT DoCoMo’s mobile phone data to conduct a detailed temporal and spatial population estimation (PE) in Futaba County from 2019 to 2020. Originally a complete evacuation zone, the area saw a partially lifted evacuation order. The suitability of the estimated population data for calculating emergency transport (ET) rates was also examined. Our findings reveal that day-to-night population ratios were significantly high in some areas; in Okuma Town and Futaba Town, the daytime population was substantially larger than the nighttime population throughout the two years, with the median day/night population ratio being more than three for both weekdays and weekends. Additionally, sex-age-adjusted ET rates in the study area, based on the estimated population, were more consistent with the national average than those calculated based on census data. This study demonstrates the critical role of PE considering temporal changes in enhancing healthcare planning and ensuring that resources are efficiently allocated to meet the evolving needs of communities during recovery periods.

ChatGPT, a large-scale language model, is a notable example of AI's potential in health care. However, its effectiveness in clinical settings, especially when compared to human physicians, is not fully understood. This study evaluates ChatGPT's capabilities and limitations in answering questions for Japanese internal medicine specialists, aiming to clarify its accuracy and tendencies in both correct and incorrect responses. We utilized ChatGPT's answers on four sets of self-training questions for internal medicine specialists in Japan from 2020 to 2023. We ran three trials for each set to evaluate its overall accuracy and performance on nonimage questions. Subsequently, we categorized the questions into two groups: those ChatGPT consistently answered correctly (Confirmed Correct Answer, CCA) and those it consistently answered incorrectly (Confirmed Incorrect Answer, CIA). For these groups, we calculated the average accuracy rates and 95% confidence intervals based on the actual performance of internal medicine physicians on each question and analyzed the statistical significance between the two groups. This process was then similarly applied to the subset of nonimage CCA and CIA questions. ChatGPT's overall accuracy rate was 59.05%, increasing to 65.76% for nonimage questions. 24.87% of the questions had answers that varied between correct and incorrect in the three trials. Despite surpassing the passing threshold for nonimage questions, ChatGPT's accuracy was lower than that of human specialists. There was a significant variance in accuracy between CCA and CIA groups, with ChatGPT mirroring human physician patterns in responding to different question types. This study underscores ChatGPT's potential utility and limitations in internal medicine. While effective in some aspects, its dependence on question type and context suggests that it should supplement, not replace, professional medical judgment. Further research is needed to integrate Artificial Intelligence tools like ChatGPT more effectively into specialized medical practices.