Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children.

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development.

Reporting quality of clinical trial protocols: a repeated cross-sectional study about the Adherence to SPIrit Recommendations in Switzerland, CAnada and GErmany (ASPIRE-SCAGE).

Objectives: Comprehensive protocols are key for the planning and conduct of randomised clinical trials (RCTs). Evidence of low reporting quality of RCT protocols led to the publication of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist in 2013. We aimed to examine the quality of reporting of RCT protocols from three countries before and after the publication of the SPIRIT checklist.