Adverse Short-Term Cardiometabolic Outcomes in Psychosis Early Intervention Services

Which Risk Prediction Algorithm?

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This study examines the role of clinical prediction models in supporting the early identification of people at risk of developing psychosis and discusses how these tools can be effectively integrated into routine mental healthcare.

The authors highlight the importance of evaluating prediction models beyond their statistical performance, considering factors such as clinical usefulness, implementation, and real-world impact. The study emphasises that successful early intervention depends not only on accurate risk assessment but also on ensuring that digital tools are practical, accessible and accepted by healthcare professionals.

By focusing on the translation of research into clinical practice, this work contributes to the development of scalable and evidence-based approaches that can support earlier detection, personalised care and improved mental health outcomes.

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