Metastatic endometrial carcinoma presenting as a scapular mass: A case report and literature review.

Bone metastases of endometrial cancers are quite rare, especially in the scapula. Only two previous reports of such cases were found in the literature, and in each case a different approach to diagnosis was used. There are no established recommendations for screening for bone metastases at diagnosis or after initial treatment of endometrial cancers. In the present case, a 55-year-old woman with progressive abdominal distension was diagnosed with a cystic mass. Histopathological analysis revealed grade II synchronous endometrioid carcinoma in both the endometrium and the ovaries. The patient received three cycles of combined paclitaxel and carboplatin chemotherapy. Seven months after the last chemotherapy cycle, a palpable lump was found in the right shoulder, suggesting a lesion in the right scapula. A bone scan revealed heightened radioactivity uptake, highlighting the unpredictable nature of the disease progression. The choice of diagnostic imaging modality remains challenging. This case emphasises the need for ongoing investigation of the mechanisms of distant metastasis and for the development of standardised diagnostic and therapeutic strategies.

Disparities in medical recommendations from AI-based chatbots across different countries/regions.

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies.

Accuracy of screening methods of COVID-19 in pregnancy: a cohort study.

BACKGROUND: All pregnant women in labor should be universally screened for Coronavirus Disease 2019 (COVID-19) during pandemic periods using reverse transcriptase polymerase chain reaction (RT-PCR) test. In many low-middle income countries, screening method was developed as an initial examination because of limited availability of RT-PCR tests. This study aims to evaluate the screening methods of COVID-19 accuracy in pregnant women. METHODS: We recruited all pregnant women with suspicion of COVID-19 from April to August 2020 at Airlangga University Hospital, Surabaya, Indonesia. The participant was divided into two groups based on RT-PCR results: COVID-19 and non-COVID-19 group. The proportion of positive signs and symptoms, rapid antibody test, abnormal findings in chest X-ray, and neutrophil to lymphocyte ratio (NLR) value were then compared between both groups. The sensitivity, specificity, positive predictive value (PPV), negative predictive values (NPV), and diagnostic accuracy (DOR) were calculated. RESULTS: A total 141 pregnant women with suspected COVID-19 cases were recruited for this study. This consist of 62 COVID-19 cases (43.9%) and 79 non-COVID-19 pregnant women (56.1%). The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of each parameter are as follow: clinical sign and symptoms (24.19%, 75.95%, 3.92%, 96.11%, 65.87%), rapid antibody test (72.73%, 35.06%, 4.35%, 96.94%, 36.53%), chest X-ray (40.68%, 59.45%, 3.92%, 96.11%, 58.76%), and NLR >5.8 (41.38%, 72%, 5.66%, 96.80%, 70.81%). CONCLUSIONS: The use of combined screening methods can classify pregnant women with high-risk COVID-19 before definitively diagnosed with RT-PCR. This practice will help to reduce RT-PCR need in a limited resources country.