Clinical and Experimental Dental Research (Apr 2021)

Craniofacial growth predictors for class II and III malocclusions: A systematic review

  • Antonio Jiménez‐Silva,
  • Romano Carnevali‐Arellano,
  • Sheilah Vivanco‐Coke,
  • Julio Tobar‐Reyes,
  • Pamela Araya‐Díaz,
  • Hernán Palomino‐Montenegro

DOI
https://doi.org/10.1002/cre2.357
Journal volume & issue
Vol. 7, no. 2
pp. 242 – 262

Abstract

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Abstract Objective To evaluate the validity of craniofacial growth predictors in class II and III malocclusion. Material and methods An electronic search was conducted until August 2020 in PubMed, Cochrane Library, Embase, EBSCOhost, ScienceDirect, Scopus, Bireme, Lilacs and Scielo including all languages. The articles were selected and analyzed by two authors independently and the selected studies was assessed using the 14‐item Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS‐2). The quality of evidence and strength of recommendation was assessed by the GRADE tool. Results In a selection process of two phases, 10 articles were included. The studies were grouped according to malocclusion growth predictor in (1) class II (n = 4); (2) class III (n = 5) and (3) class II and III (n = 1). The predictors were mainly based on data extracted from cephalometries and characterized by: equations, structural analysis, techniques and computer programs among others. The analyzed studies were methodologically heterogeneous and had low to moderate quality. For class II malocclusion, the predictors proposed in the studies with the best methodological quality were based on mathematical models and the Fishman system of maturation assessment. For class III malocclusion, the Fishman system could provide adequate growth prediction for short‐ and long‐term. Conclusions Because of the heterogeneity of the design, methodology and the quality of the articles reviewed, it is not possible to establish only a growth prediction system for class II and III malocclusion. High‐quality cohort studies are needed, well defined data extraction from cephalometries, radiographies and clinical characteristics are required to design a reliable predictor.

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