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Bone & Joint Research
Vol. 13, Issue 9 | Pages 507 - 512
18 Sep 2024
Farrow L Meek D Leontidis G Campbell M Harrison E Anderson L

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.


Bone & Joint Open
Vol. 5, Issue 2 | Pages 123 - 131
12 Feb 2024
Chen B Duckworth AD Farrow L Xu YJ Clement ND

Aims

This study aimed to determine whether lateral femoral wall thickness (LWT) < 20.5 mm was associated with increased revision risk of intertrochanteric fracture (ITF) of the hip following sliding hip screw (SHS) fixation when the medial calcar was intact. Additionally, the study assessed the association between LWT and patient mortality.

Methods

This retrospective study included ITF patients aged 50 years and over treated with SHS fixation between 2019 and 2021 at a major trauma centre. Demographic information, fracture type, delirium status, American Society of Anesthesiologists grade, and length of stay were collected. LWT and tip apex distance were measured. Revision surgery and mortality were recorded at a mean follow-up of 19.5 months (1.6 to 48). Cox regression was performed to evaluate independent risk factors associated with revision surgery and mortality.


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 Research
Vol. 12, Issue 4 | Pages 256 - 258
3 Apr 2023
Farrow L Evans J

Cite this article: Bone Joint Res 2023;12(4):256–258.


Bone & Joint Research
Vol. 11, Issue 12 | Pages 890 - 892
1 Dec 2022
Farrow L Jenkins PJ Dunstan E Murray A Blyth MJG Simpson AHRW Clement ND

Cite this article: Bone Joint Res 2022;11(12):890–892.


Bone & Joint Open
Vol. 3, Issue 10 | Pages 753 - 758
4 Oct 2022
Farrow L Clement ND Smith D Meek DRM Ryan M Gillies K Anderson L Ashcroft GP

Aims

The extended wait that most patients are now experiencing for hip and knee arthroplasty has raised questions about whether reliance on waiting time as the primary driver for prioritization is ethical, and if other additional factors should be included in determining surgical priority. Our Prioritization of THose aWaiting hip and knee ArthroplastY (PATHWAY) project will explore which perioperative factors are important to consider when prioritizing those on the waiting list for hip and knee arthroplasty, and how these factors should be weighted. The final product will include a weighted benefit score that can be used to aid in surgical prioritization for those awaiting elective primary hip and knee arthroplasty.

Methods

There will be two linked work packages focusing on opinion from key stakeholders (patients and surgeons). First, an online modified Delphi process to determine a consensus set of factors that should be involved in patient prioritization. This will be performed using standard Delphi methodology consisting of multiple rounds where following initial individual rating there is feedback, discussion, and further recommendations undertaken towards eventual consensus. The second stage will then consist of a Discrete Choice Experiment (DCE) to allow for priority setting of the factors derived from the Delphi through elicitation of weighted benefit scores. The DCE consists of several choice tasks designed to elicit stakeholder preference regarding included attributes (factors).