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Bone & Joint Open
Vol. 5, Issue 11 | Pages 953 - 961
1 Nov 2024
Mew LE Heaslip V Immins T Ramasamy A Wainwright TW

Aims. 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. Methods. 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). Results. Of the 7,201 papers reviewed, 136 included qualitative methods (0.1%). There was no significant difference between the journals, apart from Bone & Joint Open, which included 21 studies using qualitative methods, equalling 4% of its published articles. Conclusion. This study demonstrates that there is a very low number of qualitative research papers published within trauma and orthopaedic journals. Given the increasing focus on patient outcomes and improving the patient experience, it may be argued that there is a requirement to support both quantitative and qualitative approaches to orthopaedic research. Combining qualitative and quantitative methods may effectively address the complex and personal aspects of patients’ care, ensuring that outcomes align with patient values and enhance overall care quality


Bone & Joint Open
Vol. 1, Issue 5 | Pages 144 - 151
21 May 2020
Hussain ZB Shoman H Yau PWP Thevendran G Randelli F Zhang M Kocher MS Norrish A Khanduja V

Aims. The COVID-19 pandemic presents an unprecedented burden on global healthcare systems, and existing infrastructures must adapt and evolve to meet the challenge. With health systems reliant on the health of their workforce, the importance of protection against disease transmission in healthcare workers (HCWs) is clear. This study collated responses from several countries, provided by clinicians familiar with practice in each location, to identify areas of best practice and policy so as to build consensus of those measures that might reduce the risk of transmission of COVID-19 to HCWs at work. Methods. A cross-sectional descriptive survey was designed with ten open and closed questions and sent to a representative sample. The sample was selected on a convenience basis of 27 senior surgeons, members of an international surgical society, who were all frontline workers in the COVID-19 pandemic. This study was reported according to the Standards for Reporting Qualitative Research (SRQR) checklist. Results. Responses were received by all 27 surgeons from 22 countries across six continents. A number of the study respondents reported COVID-19-related infection and mortality in HCWs in their countries. Differing areas of practice and policy were identified and organized into themes including the specification of units receiving COVID-19 patients, availability and usage of personal protective equipment (PPE), other measures to reduce staff exposure, and communicating with and supporting HCWs. Areas more specific to surgery also identified some variation in practice and policy in relation to visitors to the hospital, the outpatient department, and in the operating room for both non-urgent and emergency care. Conclusion. COVID-19 presents a disproportionate risk to HCWs, potentially resulting in a diminished health system capacity, and consequently an impairment to population health. Implementation of these recommendations at an international level could provide a framework to reduce this burden


Bone & Joint Open
Vol. 4, Issue 9 | Pages 696 - 703
11 Sep 2023
Ormond MJ Clement ND Harder BG Farrow L Glester A

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.

Methods

Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.


Bone & Joint Open
Vol. 5, Issue 8 | Pages 637 - 643
6 Aug 2024
Abelleyra Lastoria DA Casey L Beni R Papanastasiou AV Kamyab AA Devetzis K Scott CEH Hing CB

Aims

Our primary aim was to establish the proportion of female orthopaedic consultants who perform arthroplasty via cases submitted to the National Joint Registry (NJR), which covers England, Wales, Northern Ireland, the Isle of Man, and Guernsey. Secondary aims included comparing time since specialist registration, private practice participation, and number of hospitals worked in between male and female surgeons.

Methods

Publicly available data from the NJR was extracted on the types of arthroplasty performed by each surgeon, and the number of procedures of each type undertaken. Each surgeon was cross-referenced with the General Medical Council (GMC) website, using GMC number to extract surgeon demographic data. These included sex, region of practice, and dates of full and specialist registration.


Bone & Joint Open
Vol. 3, Issue 10 | Pages 786 - 794
12 Oct 2022
Harrison CJ Plummer OR Dawson J Jenkinson C Hunt A Rodrigues JN

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).