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Background: In 2024, the Korean Ministry of Health and Welfare enforced a policy to increase the number of medical school students by 2,000 over the next 5 years, despite opposition from doctors. This study aims to predict the trend of excess or shortage of medical personnel in Korea due to the policy of increasing the number of medical school students by 2035.

Methods: Data from multiple sources, including the Ministry of Health and Welfare, National Health Insurance Corporation, and the Korean Medical Association, were used to estimate supply and demand.

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Background: Atrial fibrillation (AF) is the most prevalent arrhythmia encountered in clinical practice. Triglyceride glucose index (Tyg), a convenient evaluation variable for insulin resistance, has shown associations with adverse cardiovascular outcomes. However, studies on the Tyg index's predictive value for adverse prognosis in patients with AF without diabetes are lacking.

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Background: Evidence is lacking on whether chronic pain is related to the risk of cancer mortality. This study seeks to unveil the association between chronic pain and all-cause, cancer, as well as non-cancer death in cancer patients based on the National Health and Nutrition Examination Survey (NHANES) database.

Methods: Cancer survivors aged at least 20 (n = 1369) from 3 NHANES (1999-2004) cycles were encompassed.

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Background: Obesity is a risk factor for heart failure (HF) development but is associated with a lower incidence of mortality in HF patients. This obesity paradox may be confounded by unrecognized comorbidities, including cachexia.

Methods: A retrospective assessment was conducted using data from a prospectively recruiting multicenter registry, which included consecutive acute heart failure patients.

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Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.

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