To develop a multidisciplinary health research agenda (HRA) utilizing expertise from various disciplines to identify and prioritize evidence uncertainties in orthopaedics, thereby reducing research waste. We employed a novel, structured framework to develop a HRA. We started by systematically collecting all evidence uncertainties from stakeholders with an interest in orthopaedic care, categorizing them into 13 sub-themes defined by the Dutch Orthopaedic Association (NOV). Subsequently, a modified two-phased Delphi study (two rounds per phase), adhering to the Conducting and REporting DElphi Studies (CREDES) guideline, was conducted. In Phase 1, board members assessed the collected evidence uncertainties on a three-point Likert scale to confirm knowledge gaps. In Phase 2, diverse stakeholders, including orthopaedic surgeons, rated the confirmed knowledge gaps on a seven-point Likert scale. Panel members rated one self-selected sub-theme and two randomly assigned sub-themes. The results from Phase 2 were ranked based on the overall average score for each uncertainty. Finally, a focus group discussion with patient associations’ representatives identified their top-ranked uncertainty from a predefined consensus process, leading to the final HRA. An advisory board, the Federation of Medical Specialists, and the NOV research coordinator oversaw the process.Aims
Methods
Using data from the Hip Fracture Evaluation with Alternatives of Total Hip Arthroplasty versus Hemiarthroplasty (HEALTH) trial, we sought to determine if a difference in functional outcomes exists between monopolar and bipolar hemiarthroplasty (HA). This study is a secondary analysis of patients aged 50 years or older with a displaced femoral neck fracture who were enrolled in the HEALTH trial and underwent monopolar and bipolar HA. Scores from the Western Ontario and McMaster University Arthritis Index (WOMAC) and 12-Item Short Form Health Survey (SF-12) Physical Component Summary (PCS) and (MCS) were compared between the two HA groups using a propensity score-weighted analysis.Aims
Methods
To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration).Aims
Methods
