English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the explainability of the CPMs. There are discontinuities in concepts related to generalizability of CPMs in the clinical research and machine learning domains. Specifically, conventional statistical reasons to explain poor generalizability such as inadequate model development for the purposes of generalizability, differences in coding of predictors and outcome between development and external datasets, measurement error, inability to measure some predictors, and missing data, all have differing and often complementary treatments, in the two domains. Much of the current machine learning literature on generalizability of CPMs is in terms of dataset shift of which several types have been described. However, little research exists to synthesize concepts in the two domains. Bridging this conceptual discontinuity in the context of CPMs can facilitate systematic development of CPMs and evaluation of their sensitivity to factors that affect generalizability. We survey generalizability and dataset shift in CPMs from both the clinical research and machine learning perspectives, and describe a unifying framework to analyze generalizability of CPMs and to explain their sensitivity to factors affecting it. Our framework leads to a set of signaling statements that can be used to characterize differences between datasets in terms of factors that affect generalizability of the CPMs.

A Unified Framework on Generalizability of Clinical Prediction Models