The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

Executive summary Asthma is responsible for considerable global morbidity and health-care
costs. Substantial progress was made against key outcomes such as hospital admissions …

PRECIS is a tool to help trialists make design decisions consistent with the intended
purpose of their trial. This paper gives guidance on how to use an improved, validated …

Randomized controlled trials (RCTs) are conducted under idealized and rigorously
controlled conditions that may compromise their external validity. A literature review was …

Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …

Background Severe asthma is a major cause of morbidity. Some patients may benefit from
biological therapies. Most evaluations of these treatments are derived from randomized …

Evidence-based medicine (EBM) has become a required element of clinical practice, but it is
critical for the healthcare community to understand the ongoing controversy surrounding …

Clinical asthma is very widely assumed to be the net result of excessive inflammation driven
by aberrant T-helper-2 (Th2) immunity that leads to inflamed, remodelled airways and then …

Asthma in older people is common and is characterised by underdiagnosis and
undertreatment. Ageing is associated with unique issues that modify expression …

Randomised controlled trials are the best research design for decisions about the effect of
different interventions but randomisation does not, of itself, promote the applicability of a …