Joint fluid multi-omics improves diagnostic confidence during evaluation of children with presumed septic arthritis.

Background: An accurate diagnosis of septic versus reactive or autoimmune arthritis remains clinically challenging. A multi-omics strategy comprising metagenomic and proteomic technologies were undertaken for children diagnosed with presumed septic arthritis to advance clinical diagnoses and care for affected individuals.

Methods: Twelve children with suspected septic arthritis were prospectively enrolled to compare standard of care tests with a rapid multi-omics approach.

The resilience of parents and carers who administer medicines to children at home: a qualitative systematic review protocol.

Background: Parents and carers are increasingly expected to administer prescribed medicines to their children at home. However, parents and carers are not always able to administer medicines as directed by the prescriber and ultimately must rely on their own judgment to administer medicines safely. This process is often unseen but may contain important learning for professionals, academics, and wider society.

Glucose Lowering through Weight management (GLoW): a randomised controlled trial of the clinical and cost effectiveness of a diabetes education and behavioural weight management programme vs a diabetes education programme in adults with a recent diagnosis of type 2 diabetes.

Aims/hypothesis: UK standard care for type 2 diabetes is structured diabetes education, with no effects on HbA, small, short-term effects on weight and low uptake. We evaluated whether remotely delivered tailored diabetes education combined with commercial behavioural weight management is cost-effective compared with current standard care in helping people with type 2 diabetes to lower their blood glucose, lose weight, achieve remission and improve cardiovascular risk factors.

Methods: We conducted a pragmatic, randomised, parallel two-group trial.

Academic Achievement of Children With Neurofibromatosis Type 1.

Background And Objectives: Learning difficulties are frequently reported in children with neurofibromatosis type 1 (NF1), yet little is known about the extent and predictors of their academic functions across ages. We aimed to examine the developmental patterns of academic achievement in these children from childhood to adolescence and how these patterns differ across demographic and NF1-related disease factors.

Methods: This cross-sectional study integrated data of 1512 children with NF1 (mean age, 11.

Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.

Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).

Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.