Data Improvement Through Simplification: Implications for Low-Resource Settings

ConclusionsIt is assumed that the most granular data and use of the largest number of variables for risk-adjusted predictions will increase accuracy. This complexity is often at the expense of utility. Using the single best predictor in surgical outcomes research, we have shown this is not the case.  In this example, we demonstrate that one can simplify ASA into a 3-category variable without losing any ability to predict outcomes.
Source: World Journal of Surgery - Category: Surgery Source Type: research