Christopher Barton

TL;DR: InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data, is an effective tool for predicting sepsis onset and performs well even with randomly missing data.

TL;DR: High-dose epinephrine significantly improves the rate of return of spontaneous circulation and hospital admission in patients who are in prehospital cardiac arrest without increasing complications, and the increase in hospital discharge rate is not statistically significant, and no significant trend could be determined for neurological outcome.

TL;DR: Study characteristics do not predict attempts to publish research submitted to a scientific meeting, and investigators whose research is rejected by a meeting are pessimistic about chances for publication and may make less effort to publish.

TL;DR: InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.

TL;DR: This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.