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
Computer-based applications are increasingly being used by orthopaedic surgeons in their clinical practice. With the integration of technology in surgery, augmented reality (AR) may become an important tool for surgeons in the future. By superimposing a digital image on a user’s view of the physical world, this technology shows great promise in orthopaedics. The aim of this review is to investigate the current and potential uses of AR in orthopaedics. A systematic review of the PubMed, MEDLINE, and Embase databases up to January 2019 using the keywords ‘orthopaedic’ OR ‘orthopedic AND augmented reality’ was performed by two independent reviewers.Aims
Materials and Methods
The aim of this study was to determine how the short- and medium-
to long-term outcome measures after total disc replacement (TDR)
compare with those of anterior cervical discectomy and fusion (ACDF),
using a systematic review and meta-analysis. Databases including Medline, Embase, and Scopus were searched.
Inclusion criteria involved prospective randomized control trials
(RCTs) reporting the surgical treatment of patients with symptomatic
degenerative cervical disc disease. Two independent investigators
extracted the data. The strength of evidence was assessed using
the Grading of Recommendations, Assessment, Development and Evaluation
(GRADE) criteria. The primary outcome measures were overall and
neurological success, and these were included in the meta-analysis. Standardized
patient-reported outcomes, including the incidence of further surgery
and adjacent segment disease, were summarized and discussed.Aims
Patients and Methods
