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• W4282914258 abstract "Aims To introduce and assess a course using grapes as training models for ophthalmology residents to acquire basic microsurgical skills. Methods Ophthalmology residents who were novices at microsurgery were included. Participants were randomised into a 1:1 ratio to a 4-hour training programme based on fruit models (group A) or virtual reality (VR) modulator and silicone suture pads (group B), respectively. Before and after training, questionnaires were designed to measure their self-confidence with ophthalmic operations and with their coming role as surgical assistants. After training, each participant provided their interest in further studying microsurgery and was assessed for their general competence of ophthalmic microsurgery on porcine eyes. Results Eighty-three participants were included, with 42 ones in group A and 41 ones in group B. After training, participants in group A performed better in the uniformities of the suture span (p<0.05), suture thickness (p<0.05) and tissue protection (p<0.05) during the corneal suturing assessment. The overall scores of corneal suturing and circular capsulorhexis in the porcine eye in group A were comparable to those in group B (p=0.26 and 0.87, respectively). Group A showed a more positive attitude to withstand the training for more than 4 hours (p<0.001), as well as a higher willingness to receive more times of the training in the future (p<0.001). Conclusions Training models based on grapes are equal to VR simulators and silicon suture pads to provide solid training tasks for ophthalmology residents to master basic microsurgical skills, and might have advantages in lower economic cost, and easy availability. Trial registration number ChiCTR2000040439." @default.
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• W4282914258 date "2022-06-14" @default.
• W4282914258 modified "2023-10-07" @default.
• W4282914258 title "Skills assessment after a grape-based microsurgical course for ophthalmology residents: randomised controlled trial" @default.
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• W4282914258 doi "https://doi.org/10.1136/bjophthalmol-2022-321135" @default.
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