Monitoring for 5-aminosalicylate nephrotoxicity in adults with inflammatory bowel disease: prognostic model development and validation using data from the Clinical Practice Research Datalink.

Objective: To develop and validate a prognostic model for risk-stratified monitoring of 5-aminosalicylate nephrotoxicity.

Methods: This UK retrospective cohort study used data from the Clinical Practice Research Datalink Aurum and Gold for model development and validation respectively. It included adults newly diagnosed with inflammatory bowel disease and established on 5-aminosalicylic acid (5-ASA) treatment between 1 January 2007 and 31 December 2019.

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

Background: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (N).

Methods: We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022.

Development and internal validation of prognostic models for event-free survival in newly diagnosed Ewing sarcoma patients based on routinely collected clinical characteristics from the European randomised controlled trial, EE2012.

Introduction: Ewing sarcoma is a rare paediatric cancer. Currently, there is no way of accurately predicting these patients' survival at diagnosis. Disease type (ie, localised disease, lung/pleuropulmonary metastases and other metastases) is used to guide treatment decisions, with metastatic patients generally having worse outcomes than localised disease patients.