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To compare the performance of five advanced reasoning models on urology-related clinical multiple-choice questions from the MedQA dataset, and to benchmark AI performance against medical students and experienced urologists in terms of accuracy, response efficiency, and agreement patterns.

To establish a generalizable methodological framework for thinking budget optimization in medical reasoning, deriving predictive scaling laws and operationalizable efficiency regimes that enable principled computational resource allocation in clinical AI deployment. A thinking budget is the number of tokens allocated to intermediate reasoning before answer generation.

Adjuvant radiation therapy for breast cancer improves survival but may expose thoracic organs to low-dose radiation, increasing the risk of second primary lung cancer (SPLC). Racial and ethnic disparities and social factors influencing SPLC risk remain underexplored.

Early childhood caries (ECC) and saliva have been studied across disparate domains, including microbiome, fluoride, immune, oxidative-stress, and neuroendocrine research. However, the ECC-saliva literature has not previously been mapped as a connected system using modern natural language processing (NLP). This study treats PubMed titles and abstracts as data to identify major themes, emerging topics, and candidate neuroimmune axes in ECC-saliva research.

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