Aims. Early large treatment effects can arise in small studies, which lessen as more data accumulate. This study aimed to retrospectively examine whether early treatment effects occurred for two multicentre orthopaedic randomized controlled trials (RCTs) and explore biases related to this. Methods. Included RCTs were ProFHER (PROximal Fracture of the Humerus: Evaluation by Randomisation), a two-arm study of surgery versus non-surgical treatment for proximal humerus fractures, and UK FROST (United Kingdom Frozen Shoulder Trial), a three-arm study of two surgical and one non-surgical treatment for frozen shoulder. To determine whether early treatment effects were present, the primary outcome of Oxford Shoulder Score (OSS) was compared on forest plots for: the chief investigator’s (CI) site to the remaining sites, the first five sites opened to the other sites, and patients grouped in quintiles by randomization date. Potential for bias was assessed by comparing mean age and proportion of patients with indicators of poor outcome between included and excluded/non-consenting participants. Results. No bias in treatment effect was observed overall for the CI site, or the first five sites, compared with the remaining sites in either trial. An early treatment effect on the OSS was observed for the first quintile of participants recruited to ProFHER only (clinically relevant difference of seven points). Selection bias for age was observed in the ProFHER trial only, with slightly younger patients being recruited into the study. Both trials showed some selection bias for markers of poor prognosis, although these did not appear to change over time. Conclusion. No bias in treatment effects overall were found at the CI or early sites set-up. An early treatment effect was found in one of the two trials, which was likely a chance effect as this did not continue during the study. Selection bias was observed in both RCTs, however this was minimal and did not impact on outcome. Cite this article: Bone Jt Open 2023;4(2):96–103

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

Bone demonstrates good healing capacity, with a variety of strategies being utilized to enhance this healing. One potential strategy that has been suggested is the use of stem cells to accelerate healing.