Outcomes of massive transfusion recipients administered ABO-incompatible fresh frozen plasma.

Background: The provision of ABO-incompatible fresh frozen plasma (FFP) in massive transfusion (MT) has become accepted to conserve AB FFP stock. There is an evidence gap in non-trauma settings. We compare characteristics of patients who received ABO-compatible or ABO-incompatible FFP during an MT episode due to any cause of critical bleeding, and assess the impact of incompatible FFP transfusion on inhospital mortality.

Two people making sense of a story: narrative exposure therapy as a trauma intervention in early intervention in psychosis.

Narrative exposure therapy (NET) is a recommended intervention for people with multiple trauma histories; however, research is lacking into its use with people experiencing psychosis, many of whom report multiple trauma histories. This study aimed to explore experiences of NET in early intervention in psychosis (EIP) services. Eight clinicians and four experts with lived experience (experts by experience) of psychosis and multiple trauma were interviewed on a single occasion using two versions (clinician and expert by experience) of a semi-structured interview schedule.

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.