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Bone & Joint Open
Vol. 2, Issue 9 | Pages 705 - 709
1 Sep 2021
Wright J Timms A Fugazzotto S Goodier D Calder P

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.

Methods

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.


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.