Scientific Reports

Standard cognitive scores correct for age, education, and sex, but often ignore premorbid ability, race/ethnicity, and other important predictors. The C-model integrates these variables into scoring.

The model explained nearly twice as much score variance as standard scoring, improving signal over noise.

It reduced racial disparity in MCI classification by more than three-fold and improved detection in high-vocabulary participants.

Benefits replicated across independently recruited samples, supporting robustness rather than overfitting.

A scalable computer-vision pipeline scored 9,117 drawings from 2,011 adults with agreement comparable to expert raters.

Unlike black-box systems, scoring preserved element-level localization and interpretation aligned to manual protocols.

The memory trial declined with age faster than copy performance and showed reliable longitudinal sensitivity.

Touchscreen stroke data added kinetic markers such as pauses and stroke smoothness that are not visible in static scoring.