Diagnostic interpretation error of paediatric musculoskeletal (MSK) radiographs can lead to late presentation of injuries that subsequently require more invasive surgical interventions with increased risks of morbidity. We aimed to determine the radiograph factors that resulted in diagnostic interpretation challenges for emergency physicians reviewing pediatric MSK radiographs. Emergency physicians provided diagnostic interpretations on 1,850 pediatric MSK radiographs via their participation in a web-based education platform. From this data, we derived interpretation difficulty scores for each radiograph using
Diagnostic interpretation error of paediatric musculoskeletal (MSK) radiographs can lead to late presentation of injuries that subsequently require more invasive surgical interventions with increased risks of morbidity. We aimed to determine the radiograph factors that resulted in diagnostic interpretation challenges for emergency physicians reviewing pediatric MSK radiographs. Emergency physicians provided diagnostic interpretations on 1,850 pediatric MSK radiographs via their participation in a web-based education platform. From this data, we derived interpretation difficulty scores for each radiograph using
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
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