The F score ranks diagnostic tests and prediction models inconsistently with their clinical utility

The F score ranks diagnostic tests and prediction models inconsistently with their clinical utility. Moreover, the F score does not have an interpretable unit, does not allow for a comparison with a strategy assuming all are negative, and requires dichotomization of models. In contrast, standard decision-analytic measures - net benefit and decision curve analysis - allow rational and consistent choice of weighting, have an interpretable unit, can evaluate the strategy of assuming all are negative, and do not require dichotomization of continuous models. Consistent with TRIPOD AI we recommend that net benefit, alongside discrimination and calibration, be used for the evaluation of diagnostic tests and prediction models.