Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival

The paradigm is the established understanding of melanoma outcomes prediction within the oncological field, with the focus on improving prognostic accuracy through existing frameworks.

This research operates within the 'normal science' stage of Kuhn's paradigm cycle. It accepts the existing paradigm of using clinicopathological data and established survival metrics (RFS, DSS, OS) for melanoma prognosis. The study explores refinements within this paradigm by comparing machine learning models to improve prediction accuracy. It does not challenge fundamental assumptions or introduce radically new concepts but rather seeks to optimize current methodologies, making it a clear example of puzzle-solving activity within a defined scientific framework. While there's some hint of potential model drift concerning overall survival outcomes, the study's primary focus remains within the boundaries of accepted knowledge and methodologies.