Improving outcomes for very preterm babies in England: does place of birth matter? Findings from OPTI-PREM, a national cohort study.

Objective: Babies born between 27 and 31 weeks of gestation contribute substantially towards infant mortality and morbidity. In England, their care is delivered in maternity services colocated with highly specialised neonatal intensive care units (NICU) or less specialised local neonatal units (LNU). We investigated whether birth setting offered survival and/or morbidity advantages to inform National Health Service delivery.

Challenges and opportunities in perinatal public health: the utility of perinatal health inequality dashboards in addressing disparities in maternal and neonatal outcomes.

Introduction: In clinical settings, digital dashboards display medical data, with the aim of identifying trends and signals. In so doing these contribute towards improving service delivery and care within hospitals. It is not clear whether the utility of perinatal health equity dashboards could be used to identify health inequality trends that could potentially impact on health service delivery, care and public health interventions.

Pre-arthritic coronal plane alignment predicted by the arithmetic hip-knee-ankle angle (aHKA) and the Flexion Extension Balancing Algorithm (FEBA) for Primary Total Knee Arthroplasty (TKA).

Background: Given the relative rate of dissatisfaction following total knee arthroplasty (TKA) and the need to further improve outcomes for all patients, various surgical methods have been developed that aim to restore the pre-arthritic alignment of the knee and lower limb. Common to these methodologies is a need to determine the pre-arthritic alignment of the knee and limb, thus producing defined targets for surgery. The aim of this paper was to compare the predicted pre-arthritic constitutional alignment of knee and lower limb calculated by the Flexion Extension Balancing Algorithm (FEBA) and the arithmetic HKA (aHKA) methods.

Explainable Artificial Intelligence in Paediatric: Challenges for the Future.

Background: Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences.