Evidence-appraisal glossary
Calibration Slope
The calibration slope measures whether a risk model's predictions are too extreme or too timid. A slope of one is ideal; a slope below one means high predictions run too high and low predictions run too low.
You obtain it by regressing the actual outcomes on the predicted risks on the log-odds scale and reading off the slope. A slope under one is the fingerprint of overfitting, where a model absorbed noise from its development data and spread its predictions too widely. Checking the slope in fresh data tells you how much a model's confidence must be reined in before it can be trusted.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.