Key result
CV prediction models individualize treatment but face real-world limitations from calibration drift and biases.
Why the study?
External validation of clinical prediction models frequently reveals a discrepancy between statistical performance and real-world implementation due to calibration drift, geographic transportability failures, and systemic biases.
Design
Review
Authors
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May aid individualized decisions alongside RCTs; leaves open solutions for calibration drift and biases.
Clinical prediction models are vital complementary tools to randomized trials for individualizing cardiovascular treatment decisions, though real-world implementation is often hindered by calibration drift and biases.
Wang et al. (2026) conducted a review in Cardiovascular disease. Clinical prediction models was evaluated. Clinical prediction models in cardiovascular disease serve as vital tools to individualize treatment decisions, though real-world implementation is often limited by calibration drift and biases.
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