Continuous Scores from Categorical Labels via Low-Capacity Networks

A neural network with a bottleneck and category approximator head derives continuous patient severity scores from three categorical outcomes. The method uses a single dense layer with no activation to prevent score contortion, with trainable thresholds from softmax weights. Code is available on Codeberg.