Abstract
We argue that individual baselines are essential to interpreting mast cell activation signals, and quantify how personalization improves detection over population norms.
No average patient
In MCAS, what counts as "elevated" for one person may be normal for another. Population averages can obscure exactly the deviations that matter for an individual.
Personalization in practice
By learning each person's own baseline and typical variation, models can flag deviations that are meaningful for that individual — improving both sensitivity and specificity relative to fixed thresholds.
Design consequence
This is why the MCT - 1 and app are built to calibrate to you, not to a generic norm.
Related: explore the MCT - 1 biosensor and our research overview.