Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Changes in walking speed following resistance training in people with multiple sclerosis: A systematic review and meta-analysis.

Background: Reduced walking ability, especially decreased gait speed, is one of the most common and disabling impairments reported by people with multiple sclerosis (MS). Considering the impact of muscle strength on walking ability, resistance training may have the potential to improve walking speed in MS. Therefore, this systematic review and meta-analysis aims to evaluate the effect of lower limb resistance training on walking speed in people with MS.