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Objective: Poorly differentiated thyroid cancer (PDTC) is a rare, heterogeneous carcinoma from follicular cells, characterized by poor differentiation, aggressive spread, and poor prognosis. Currently, there is no specific staging system for PDTC. This study aimed to develop a new TNM staging system tailored to PDTC for improved disease management.
Methods: A new TNM staging system was designed and internally validated using data from the US SEER database (2004-2016) on PDTC cases. External validation was performed using data from four major institutions in China. Prognostic factors influencing cancer-specific survival (CSS) were identified through Cox regression analyses. Patients were stratified into subgroups based on adjusted hazard ratios (AHRs), weighted by these prognostic factors. The new system classified patients into five stages with distinct 5-year CSS outcomes.
Results: The study analyzed 1,201 PDTC cases from SEER and 85 cases from China. Among the 876 patients in the training cohort, the new TNM staging system showed superior discrimination compared to the 8th edition of the AJCC TNM system. The 5-year CSS rates for the new stages I, II, III, IVA, and IVB were 96.3%, 88.4%, 69.4%, 43.3%, and 22.3%, respectively. The new system outperformed the 8th edition in predicting CSS, as shown by time-dependent ROC curves, C-index, and calibration plots. Both internal and external validation confirmed its predictive abilities.
Conclusion: The current AJCC staging system inadequately predicts PDTC prognosis. The new TNM staging system developed in this study offers improved stratification and prognosis prediction, potentially guiding more effective clinical management for PDTC.
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http://dx.doi.org/10.3389/fendo.2025.1586542 | DOI Listing |
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
J Med Internet Res
September 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Mental and behavioral disorders affect approximately 28% of the adult population in Germany per year, with treatment being provided through a diverse health care system. Yet there are access and capacity problems in outpatient mental health care. One innovation that could help reduce these barriers and improve the current state of care is the use of mobile health (mHealth) apps, known in Germany as Digitale Gesundheitsanwendungen (DiGA).
View Article and Find Full Text PDFJMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
Age Ageing
August 2025
Department of Social Determinants of Health, Division of Healthier Populations, World Health Organization, Geneva, Switzerland.
The Abuse of Older People - Intervention Accelerator (AOP-IA) project aims to accelerate the development of effective interventions to prevent and reduce AOP aged 60 and older within the framework of the United Nations Decade of Healthy Ageing (2021-2030). The AOP-IA was launched in response to the global need for interventions with proven effectiveness, as few existing approaches have been rigorously evaluated. This paper focuses on the first two phases of the AOP-IA project, which involved conducting a systematic search, screening and evaluation process to identify candidate interventions ready to be rigorously evaluated in future stages of the project, as well as establishing a network of intervention developers.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
Department of Chemistry and Chemical Engineering, Engineering Research Center of Forestry Biomass Materials and Bioenergy (Ministry of Education), National Forest and Grass Administration Woody Spices (East China) Engineering Technology Research Center, Beijing Forestry University, Beijing 100083, C
This study develops a catalytic system using pyruvic acid (PYA) and Fe to efficiently coproduce xylo-oligosaccharides (XOS) and (manno-oligosaccharides) MOS from food material ( Lam. fruit.) and its waste peel, respectively.
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