Good predictive models are an asset in medicine. Ultimately, a model is a "simplification or approximation of reality", [1,2] but by distilling complicated data into the chance of a given outcome occurring, such models support clinical and shared decision-making. Reliable model development and validation are crucial in creating a good predictive model, and this requires -among other things-formal statistical incorporation of each prognostically important variable into the model. [2][3][4] We consider recent discussions surrounding the use of the race and/or ethnicity (R&E) in medicine to be of utmost importance. [5][6][7] We add to these discussions by noting the fracture risk assessment tool (FRAX ® ) is importantly problematic in its handling of R&E.FRAX ® formally incorporates many variables into a model aiming to predict a person's 10-year risk of hip or major osteoporotic fracture (MOF). The variables include age, sex, body mass index, history of osteoporotic fracture, parental history of hip fracture, current smoking, current or previous glucocorticoid use for >3 months at a prednisolone (or equivalent) dose of ≥5 mg/day, rheumatoid arthritis, secondary osteoporosis, ≥3 servings of alcohol per day, and bone mineral density (BMD). In contrast to what FRAX ® does for most countries, the FRAX ® model for the USA (FRAX ® -USA) offers different risk estimates based on R&E. Unfortunately, R&E was never formally evaluated as part of the modeling effort or incorporated into the FRAX ® -USA model; thus, FRAX ® failed to determine whether R&E have any independent predictive value when considered concurrently with all the other variables in FRAX ® -USA. Instead, the base model was built using data from people who were predominantly White. If the user indicates the person is White, the base model risk is returned as is. For the options of Asian, Black, or Hispanic, FRAX ® -USA applies to the base model post hoc "correction factors" (ranging from 0.43 to 0.64) that are de facto offsets that were derived outside the multivariable predictive model. [8][9][10][11] Such an approach is below established standards for modeling and results in R&E having extreme influence on predicted fracture risk.For example, a female 65 years of age with a low-trauma wrist fracture who weighs 140 pounds, measures 65 inches tall, and has a femoral neck T-score of -2.5 is estimated to have a 21% risk of MOF in the next 10 years if she is White. Changing