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A review of participant recruitment transparency for sound validation of hip surgery simulators: a novel umbrella approach.

Samaratunga, R., Johnson, L., Gatzidis, C., Swain, I. D., Wainwright, T. and Middleton, R., 2021. A review of participant recruitment transparency for sound validation of hip surgery simulators: a novel umbrella approach. Journal of Medical Engineering and Technology, 45 (6), 434-456.

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DOI: 10.1080/03091902.2021.1921868

Abstract

Malposition of implants is associated with complications, higher wear and increased revision rates in total hip replacement (THR) along with surgeon inexperience. Training THR residents to reach expert proficiency is affected by the high cost and resource limitations of traditional training techniques. Research in extended reality (XR) technologies can overcome such barriers. These offer a platform for learning, objective skill-monitoring and, potentially, for automated certification. Prior to their incorporation into curricula however, thorough validation must be undertaken. As validity is heavily dependent on the participants recruited, there is a need to review, scrutinise and define recruitment criteria in the absence of pre-defined standards, for sound simulator validation. A systematic review on PubMed and IEEE databases was conducted. Training simulator validation research in fracture, arthroscopy and arthroplasty relating to the hip was included. 46 validation studies were reviewed. It was observed that there was no uniformity in reporting or recruitment criteria, rendering cross-comparison challenging. This work developed Umbrella categories to help prioritise recruitment, and has formulated a detailed template of fields and guidelines for reporting criteria so that, in future, research may come to a consensus as to recruitment criteria for a hip “expert” or “novice”.

Item Type:Article
ISSN:0309-1902
Uncontrolled Keywords:Hip; VR; simulation; training; validation; Arthroplasty, Replacement, Hip; Clinical Competence; Humans
Group:Faculty of Science & Technology
ID Code:38357
Deposited By: Symplectic RT2
Deposited On:15 Mar 2023 09:46
Last Modified:15 Mar 2023 09:46

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