Calibration¶
Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019). Calibration: The Achilles Heel of Predictive Analytics. BMC Medicine.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Label Shift
- But the probability outputs and the alerting threshold, calibrated to the referral prevalence, now fire far too often on the low-prevalence population, generating a wave of false alarms and alert fatigue
This sourceA calibration review showing that models developed where disease incidence is high systematically overestimate risk in lower-incidence settings, leading to overtreatment.
Supported in partVerified against the work's full text
“When an algorithm is developed in a setting with a high disease incidence, it may systematically give overestimated risk estimates when used in a setting where the incidence is lower”
- But the probability outputs and the alerting threshold, calibrated to the referral prevalence, now fire far too often on the low-prevalence population, generating a wave of false alarms and alert fatigue
Mechanisms¶
- Clinical Risk Banding
- The discipline that keeps it honest is to check calibration in the local population
This sourceRequires calibration evaluation in the intended clinical population before predictions guide care and continued local monitoring as the target population drifts.
- The discipline that keeps it honest is to check calibration in the local population
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
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Registry ID ref:6d9ddf38e066 · see in the full table