Spatial transcriptomics implicates impaired BMP signaling in NF1 fracture pseudarthrosis in murine and patient tissues.

The neurofibromatosis type 1 (NF1) RASopathy is associated with persistent fibrotic nonunions (pseudarthrosis) in human and mouse skeletal tissue. Here, we performed spatial transcriptomics to define the molecular signatures occurring during normal endochondral healing following fracture in mice. Within the control fracture callus, we observed spatially restricted activation of morphogenetic pathways, such as TGF-β, WNT, and BMP.

Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.

Purpose: Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims.

Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review.

Purpose: Through electronic health records (EHRs), musculoskeletal (MSK) therapists such as chiropractors and physical therapists, as well as occupational medicine physicians could collect data on many variables that can be traditionally challenging to collect in managing work-related musculoskeletal disorders (WMSDs). The review's objectives were to explore the extent of research using EHRs in predicting outcomes of WMSDs by MSK therapists.

Method: A systematic search was conducted in Medline, PubMed, CINAHL, and Embase.

Electronic Health Records Role in Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Study Protocol.

Work-related musculoskeletal disorders are increasing in cost and time lost from work. Electronic health records have the potential to provide rich data to help inform and predict outcomes to WMSDs. The objective is to compare an EHR dataset from an occupational health service to comparative data, to help determine if the EHR dataset can be used in future studies to predict outcomes to care.

Understanding Clinician EHR Data Quality for Reuse in Predictive Modelling.

It is imperative to build clinician trust to reuse ever-growing amounts of rich clinical data. Utilising a proprietary, structured electronic health record, we address data quality by assessing the plausibility of chiropractors, physical therapists and osteopaths' data entry to help determine if the data is fit for use in predicting outcomes of work-related musculoskeletal disorders using machine learning. For most variables assessed, individual clinician data entry positively correlated to the clinician group's data entry, indicating data is fit for reuse.