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National Journal of Maxillofacial Surgery
 
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ORIGINAL ARTICLE
Year : 2019  |  Volume : 10  |  Issue : 2  |  Page : 206-211

Decision-making in classifying gingival recession defects – A systematic review


Department of Periodontology, Himachal Pradesh Government Dental College and Hospital, Shimla, Himachal Pradesh, India

Correspondence Address:
Dr. Deepa Rayast
Department of Periodontology, Himachal Pradesh Government Dental College and Hospital, Shimla - 171 001, Himachal Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/njms.NJMS_71_18

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Background: Various classification systems have been devised to classify gingival recession defects (GRDs). Recent evidence has raised many questions on the use of currently popular classification systems. The purpose of this systematic review is to assess various classification systems in the light of the current scientific literature. Methods: A comprehensive and systematic search was done to identify literature related to classification systems for GRD. Sources included books, journals, and online database. The search was done using the predefined criteria; 337 articles were initially identified through online database PubMed (Medline) and 12 from handsearch, of which a total of 10 full text articles were finally selected. Results: The classification systems which were included in the review included the classifications given by Sullivan and Atkins, Miller, Smith, Nordland and Tarnow, Kumar and Masamatti, and Mahajan. The systematic review revealed that the Sullivan and Atkins classification system for gingival recession was the most useful classification system for clinicians till the year 1985 in which P. D. Miller introduced the classification system for marginal tissue recession. From 1985 to till date, the Miller's classification system is the most frequently used and popular classification system. Conclusion: None of the classification systems for GRD fulfilled the ideal criteria; however, some of the recently introduced classification systems have evolved as a more comprehensive and viable alternative to already established classification systems.


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