Hand trauma, consisting of injuries to both the hand and the wrist, are a common injury seen worldwide. The global age-standardized incidence of hand trauma exceeds 179 per 100,000. Hand trauma may require surgical management and therefore result in significant costs to both healthcare systems and society. Surgical site infections (SSIs) are common following all surgical interventions, and within hand surgery the risk of SSI is at least 5%. SSI following hand trauma surgery results in significant costs to healthcare systems with estimations of over £450 per patient. The World Health Organization (WHO) have produced international guidelines to help prevent SSIs. However, it is unclear what variability exists in the adherence to these guidelines within hand trauma. The aim is to assess compliance to the WHO global guidelines in prevention of SSI in hand trauma. This will be an international, multicentre audit comparing antimicrobial practices in hand trauma to the standards outlined by WHO. Through the Reconstructive Surgery Trials Network (RSTN), hand surgeons across the globe will be invited to participate in the study. Consultant surgeons/associate specialists managing hand trauma and members of the multidisciplinary team will be identified at participating sites. Teams will be asked to collect data prospectively on a minimum of 20 consecutive patients. The audit will run for eight months. Data collected will include injury details, initial management, hand trauma team management, operation details, postoperative care, and antimicrobial techniques used throughout. Adherence to WHO global guidelines for SSI will be summarized using descriptive statistics across each criteria.Aims
Methods
The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents’ full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson’s correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments’ minimal clinically important difference (MCID).Aims
Methods
