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Published on: November 2, 2012
Asking the PRIME questions for grossing: Teaching a framework for grossing and constructing gross descriptions using
Christopher C Attaway1,2,3, Danielle Fortuna1
1Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Abstract:
Grossing is essential to the practice of anatomic pathology. The importance of this skill cannot be understated, but it simultaneously can be enigmatic for novice pathology residents. Successful grossing asks questions to yield the most accurate answers which facilitate a complete report and diagnosis for patient care. To provide a unified framework of approach to grossing specimens, we devised the PRIME (P = process/picture, R = relationships, I = internal, M = margins, E = external) model for grossing. The PRIME model was introduced to anatomic pathology trainees (n = 21) at two academic hospitals through an interactive workshop featuring multiple exercises: (1) scoring provided inadequate gross descriptions of common, familiar objects (fruit) for content quality before and after introduction of the PRIME model, (2) building a gross description as a group with a representative fruit specimen using PRIME, (3) videos of grossing specimens which the participants used to practice constructing their own gross description using PRIME, and (4) analysis of an example surgical specimen's gross description using PRIME. Pre- and post-workshop questionnaires assessed the trainees' experience with grossing before residency, their confidence to write a gross description, and their opinions of the PRIME model. The assessment of fruit gross descriptions before and after the introduction of PRIME was significant (p < 0.05), as well as the participants' confidence level to write an accurate gross description using PRIME. The PRIME model and workshop help to fill a void in pathology education and erode perceived barriers to confident grossing by providing a framework of the key concepts behind grossing specimens, no matter the complexity.
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