The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature. A bibliometric search of journals’ online archives and multiple databases was undertaken in March 2024, to identify articles using qualitative research methods in the top 12 trauma and orthopaedic journals based on the 2023 impact factor and SCImago rating. The bibliometric search was conducted and reported in accordance with the preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO).Aims
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It is well described that patients with bone and joint infections (BJIs) commonly experience significant functional impairment and disability. Published literature is lacking on the impact of BJIs on mental health. Therefore, the aim of this study was to assess health-related quality of life (HRQoL) and the impact on mental health in patients with BJIs. The AO Trauma Infection Registry is a prospective multinational registry. In total, 229 adult patients with long-bone BJI were enrolled between 1 November 2012 and 31 August 2017 in 18 centres from ten countries. Clinical outcome data, demographic data, and details on infections and treatments were collected. Patient-reported outcomes using the 36-Item Short-Form Health Survey questionnaire (SF-36), Parker Mobility Score, and Katz Index of Independence in Activities of Daily Living were assessed at one, six, and 12 months. The SF-36 mental component subscales were analyzed and correlated with infection characteristics and clinical outcome.Aims
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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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Deprivation underpins many societal and health inequalities. COVID-19 has exacerbated these disparities, with access to planned care falling greatest in the most deprived areas of the UK during 2020. This study aimed to identify the impact of deprivation on patients on growing waiting lists for planned care. Questionnaires were sent to orthopaedic waiting list patients at the start of the UK’s first COVID-19 lockdown to capture key quantitative and qualitative aspects of patients’ health. A total of 888 respondents were divided into quintiles, with sampling stratified based on the Index of Multiple Deprivation (IMD); level 1 represented the ‘most deprived’ cohort and level 5 the ‘least deprived’.Aims
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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 SLRS was designed following structured interviews with several groups including patients who have undergone limb reconstruction surgery, limb reconstruction surgeons, specialist nurses, and physiotherapists. This has subsequently undergone further adjustment for language and clarity. The score was then trialled on ten patients who had undergone limb reconstruction surgery, with subsequent structured questioning to understand the perceived suitability of the score.Aims
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Elective orthopaedic services have had to adapt to significant system-wide pressures since the emergence of COVID-19 in December 2019. Length of stay is often recognized as a key marker of quality of care in patients undergoing arthroplasty. Expeditious discharge is key in establishing early rehabilitation and in reducing infection risk, both procedure-related and from COVID-19. The primary aim was to determine the effects of the COVID-19 pandemic length of stay following hip and knee arthroplasty at a high-volume, elective orthopaedic centre. A retrospective cohort study was performed. Patients undergoing primary or revision hip or knee arthroplasty over a six-month period, from 1 July to 31 December 2020, were compared to the same period in 2019 before the COVID-19 pandemic. Demographic data, American Society of Anesthesiologists (ASA) grade, wait to surgery, COVID-19 status, and length of hospital stay were recorded.Aims
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COVID-19 has compounded a growing waiting list problem, with over 4.5 million patients now waiting for planned elective care in the UK. Views of patients on waiting lists are rarely considered in prioritization. Our primary aim was to understand how to support patients on waiting lists by hearing their experiences, concerns, and expectations. The secondary aim was to capture objective change in disability and coping mechanisms. A minimum representative sample of 824 patients was required for quantitative analysis to provide a 3% margin of error. Sampling was stratified by body region (upper/lower limb, spine) and duration on the waiting list. Questionnaires were sent to a random sample of elective orthopaedic waiting list patients with their planned intervention paused due to COVID-19. Analyzed parameters included baseline health, change in physical/mental health status, challenges and coping strategies, preferences/concerns regarding treatment, and objective quality of life (EuroQol five-dimension questionnaire (EQ-5D), Generalized Anxiety Disorder 2-item scale (GAD-2)). Qualitative analysis was performed via the Normalization Process Theory.Aims
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Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care. This was a cross-sectional study of patients from Duke University Department of Orthopaedic Surgery clinics (14 ambulatory and four hospital-based). There were two separate cohorts recruited by convenience sampling (i.e. patients were included in the analysis only if they completed PROMIS measures during a new patient visit). Cohort #1 (n = 12,141; December 2017 to December 2018,) included PROMIS short forms for eight domains (Physical Function, Pain Interference, Pain Intensity, Depression, Anxiety, Sleep Quality, Participation in Social Roles, and Fatigue) and Cohort #2 (n = 4,638; January 2019 to August 2019) included PROMIS Computer Adaptive Testing instruments for four domains (Physical Function, Pain Interference, Depression, and Sleep Quality). Cluster analysis (K-means method) empirically derived subgroups and subgroup differences in clinical and sociodemographic factors were identified with one-way analysis of variance.Aims
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