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
The objective of this study was to explore dimensionality of
the Oxford Hip Score (OHS) and examine whether self-reported pain
and functioning can be distinguished in the form of subscales. This was a secondary data analysis of the UK NHS hospital episode
statistics/patient-reported outcome measures dataset containing
pre-operative OHS scores on 97 487 patients who were undergoing
hip replacement surgery. Objective
Methods
The Manchester–Oxford Foot Questionnaire (MOXFQ) is a validated
16-item, patient-reported outcome measure for evaluating outcomes
of foot or ankle surgery. The original development of the instrument
identified three domains. This present study examined whether the
three domains could legitimately be summed to provide a single summary
index score. The MOXFQ and Short-Form (SF)-36 were administered to 671 patients
before surgery of the foot or ankle. Data from the three domains
of the MOXFQ (pain, walking/standing and social interaction) were
subjected to higher order factor analysis. Reliability and validity
of the summary index score was assessed.Objectives
Methods
