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Effect of Histopathological Explanations for Dermoscopic Criteria on Learning Curves in Skin Cancer Training: a Randomized Controlled Trial

Case-based training improves novices pattern recognition and diagnostic accuracy in skin cancer diagnostics. However, it is unclear how pattern recognition is best taught in conjunction with the knowledge needed to justify a diagnosis. The aim of this study was to examine whether an explanation of t...

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Bibliographic Details
Published in:Dermatology practical & conceptual 2023-04, Vol.13 (2), p.e2023105
Main Authors: Kvorning Ternov, Niels, Tolsgaard, Martin, Konge, Lars, Christensen, Anders Nymark, Kristensen, Sigrid Isabella Pilgaard, Hölmich, Lisbet Rosenkrantz, Stretch, Jonathan, Scolyer, Richard Anthony, Vestergaard, Tine, Guitera, Pascale, Chakera, Annette Hougaard
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Language:English
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Summary:Case-based training improves novices pattern recognition and diagnostic accuracy in skin cancer diagnostics. However, it is unclear how pattern recognition is best taught in conjunction with the knowledge needed to justify a diagnosis. The aim of this study was to examine whether an explanation of the underlying histopathological reason for dermoscopic criteria improves skill acquisition and retention during case-based training in skin cancer diagnostics. In this double-blinded randomized controlled trial, medical students underwent eight days of case-based training in skin cancer diagnostics, which included access to written diagnosis modules. The modules dermoscopic subsections differed between the study groups. All participants received a general description of the criteria, but the intervention group additionally received a histopathological explanation. Most participants (78%) passed a reliable test in skin cancer diagnostics, following a mean training time of 217 minutes. Access to histopathological explanations did not affect participants' learning curves or skill retention. The histopathological explanation did not affect the students, but the overall educational approach was efficient and scalable.
ISSN:2160-9381
2160-9381
DOI:10.5826/dpc.1302a105