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Use of computer‐aided holographic models improves performance in a cadaver dissection‐based course in gross anatomy
Author(s) -
Miller Michael
Publication year - 2016
Publication title -
clinical anatomy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.667
H-Index - 71
eISSN - 1098-2353
pISSN - 0897-3806
DOI - 10.1002/ca.22766
Subject(s) - gross anatomy , cadaver , dissection (medical) , medicine , test (biology) , anatomy , medical physics , medical education , biology , paleontology
A compelling, innovative approach to teaching gross anatomy is the use of computer‐aided holographic models. They allow for clean, time‐saving dissection, manipulation of structures and appreciation of anatomical relationships, and potential elimination of the need for cadavers. The present study tests the hypothesis that using holographic models improves mastery of anatomical information. First‐year medical students were taught gross anatomy using the dissection of donor cadavers, manipulation of digitized 3‐dimensional holographic renderings, and examination of plastinated specimens. The effectiveness of these approaches was assessed by comparing students' performance on identification questions on cadavers (qC), holographic models (qH), and plastinated specimens (qP). Students in the top quintile of the class performed strongly on qC, qH, and qP. In contrast, performance by students in the bottom quintile was uneven; they scored best on qH. Performance on the qP was relatively non‐discriminating. Students in the top quintile scored higher on the biological sciences section of the Medical College Admission Test (MCAT) than students in the lowest quintile, whereas students in the lowest quintile had higher scores on the verbal section of their MCATs. The availability of different approaches for presenting gross anatomy improves the success of students in mastering the material, particularly for students struggling with the information. The use of holographic models apparently reaches students who may be challenged to learn the material using traditional approaches. This may be linked to potentially predictive information gleaned through performance on the MCAT. Clin. Anat. 29:917–924, 2016. © 2016 Wiley Periodicals, Inc.