Aims. 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. Methods. 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). Results. The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson’s correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient’s CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. Conclusion. Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy. Cite this article: Bone Jt Open 2022;3(10):786–794

Aims

Patients undergoing limb reconstruction surgery often face a challenging and lengthy process to complete their treatment journey. The majority of existing outcome measures do not adequately capture the patient-reported outcomes relevant to this patient group in a single measure. Following a previous systematic review, the Stanmore Limb Reconstruction Score (SLRS) was designed with the intent to address this need for an effective instrument to measure patient-reported outcomes in limb reconstruction patients. We aim to assess the face validity of this score in a pilot study.

The Oxford hip and knee scores are used to measure the outcome after primary total hip and knee replacement. We propose a new layout for the instrument in which patients are always asked about both limbs. In addition, we have defined an alternative scoring method which accounts for missing data. Over a period of 4.5 years, 4086 (1423 patients) and 5708 (1458 patients) questionnaires were completed for hips and knees, respectively. The hip score had a pre-operative median of 70.8 (interquartile range (IQR) 58.3 to 81.2) decreasing to 20.8 (IQR 10.4 to 35.4) after one year. The knee score had a pre-operative median of 68.8 (IQR 56.2 to 79.2) decreasing to 29.2 (IQR 14.6 to 45.8). There was no further significant change in either score after one year. As a result of the data analysis, we suggest that the score percentiles can be used as a standard for auditing patients before and after operation.