Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
Methods
The aim of this systematic review and meta-analysis was to gather epidemiological information on selected musculoskeletal injuries and to provide pooled injury-specific incidence rates. PubMed (National Library of Medicine) and Scopus (Elsevier) databases were searched. Articles were eligible for inclusion if they reported incidence rate (or count with population at risk), contained data on adult population, and were written in English language. The number of cases and population at risk were collected, and the pooled incidence rates (per 100,000 person-years) with 95% confidence intervals (CIs) were calculated by using either a fixed or random effects model.Aims
Methods
Biofilm-related infection is a major complication that occurs in orthopaedic surgery. Various treatments are available but efficacy to eradicate infections varies significantly. A systematic review was performed to evaluate therapeutic interventions combating biofilm-related infections on in vivo animal models. Literature research was performed on PubMed and Embase databases. Keywords used for search criteria were “bone AND biofilm”. Information on the species of the animal model, bacterial strain, evaluation of biofilm and bone infection, complications, key findings on observations, prevention, and treatment of biofilm were extracted.Aims
Methods
A balanced inflammatory response is important for successful fracture healing. The response of osteoporotic fracture healing is deranged and an altered inflammatory response can be one underlying cause. The objectives of this review were to compare the inflammatory responses between normal and osteoporotic fractures and to examine the potential effects on different healing outcomes. A systematic literature search was conducted with relevant keywords in PubMed, Embase, and Web of Science independently. Original preclinical studies and clinical studies involving the investigation of inflammatory response in fracture healing in ovariectomized (OVX) animals or osteoporotic/elderly patients with available full text and written in English were included. In total, 14 articles were selected. Various inflammatory factors were reported; of those tumour necrosis factor-α (TNF-α) and interleukin (IL)-6 are two commonly studied markers. Preclinical studies showed that OVX animals generally demonstrated higher systemic inflammatory response and poorer healing outcomes compared to normal controls (SHAM). However, it is inconclusive if the local inflammatory response is higher or lower in OVX animals. As for clinical studies, they mainly examine the temporal changes of the inflammatory stage or perform comparison between osteoporotic/fragility fracture patients and normal subjects without fracture. Our review of these studies emphasizes the lack of understanding that inflammation plays in the altered fracture healing response of osteoporotic/elderly patients. Taken together, it is clear that additional studies, preclinical and clinical, are required to dissect the regulatory role of inflammatory response in osteoporotic fracture healing. Cite this article:
