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Margin of error: accuracy of estimated excision margins by surgical experience
Author(s) -
Harriet K Semple,
Marc Langbart
Publication year - 2022
Publication title -
australasian journal of plastic surgery
Language(s) - English
Resource type - Journals
ISSN - 2209-170X
DOI - 10.34239/ajops.v5n1.257
Subject(s) - margin (machine learning) , surgical excision , medicine , confidence interval , lesion , surgery , computer science , machine learning
Guidelines for recommended margins for common lesions are well documented. While it is recommended that all margins be measured prior to excision, time pressures, lack of equipment or clinician confidence may result in margins that are estimated rather than formally measured. This increases the risk of involved margins and need for re-excision to prevent recurrence. We reviewed the estimated margins of common excisions and compared these between groups of different surgical experience. We found that while accuracy generally improves with surgical experience, margins are largely underestimated by all groups. We hope to encourage the use of formally measured margins in all lesion excisions.  

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