Leixuri de Frutos-Valle1, Conchita Martin2, Jose Antonio Alarcon3, Juan Carlos Palma-Fernandez1, Alejandro Iglesias-Linares4. 1. Section of Orthodontics, Faculty of Odontology, Complutense University, Madrid, Spain. 2. Section of Orthodontics, Faculty of Odontology, Complutense University, Madrid, Spain; BIOCRAN (Craniofacial Biology) Research Group, Complutense University, Madrid, Spain. Electronic address: mariacom@ucm.es. 3. BIOCRAN (Craniofacial Biology) Research Group, Complutense University, Madrid, Spain; Faculty of Odontology, University of Granada, Campus Universitario de Cartuja, Granada, Spain. 4. Section of Orthodontics, Faculty of Odontology, Complutense University, Madrid, Spain; BIOCRAN (Craniofacial Biology) Research Group, Complutense University, Madrid, Spain.
Abstract
OBJECTIVE: We aimed to systematically review articles investigating the efficiency of the clustering of skeletal class III malocclusion phenotypic subtypes of different ethnic origins as a diagnostic tool. METHODS: The review protocol was structured in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in Prospero (CRD42016053865). A survey of articles published up to March 2018 investigating the identification of different subgroups of skeletal class III malocclusion via cluster analysis was performed using 11 electronic databases. Any type of study design that addressed the classification of subclusters of class III malocclusion was considered. The Newcastle-Ottawa scale for cohort and cross-sectional (modified) studies was used for quality assessment. RESULTS: The final selection included 7 studies that met all the criteria for eligibility (% overall agreement 0.889, free marginal kappa 0.778). All studies identified at least 3 different types of class III clusters (ranging from 3 to 14 clusters; the total variation of the prevalence of each cluster ranged from 0.2% to 36.0%). The main shared variables used to describe the more prevalent clusters in the studies included were vertical measurements (Ar-Go-Me: 117.51°-135.8°); sagittal measurements: maxilla (SNA: 75.3°-82.95°), mandible (SNB: 77.03°-85.0°). With regard to ethnicity, a mean number of 8.5 and 3.5 clusters of class III were retrieved for Asian and Caucasian population, respectively. CONCLUSIONS: The total number of clusters identified varied from 3 to 14 to explain all the variability in the phenotype class III malocclusions. Although each extreme may be too simple or complex to facilitate an exhaustive but useful classification for clinical use, a classification system including 4 to 7 clusters may prove to be efficient for clinical use in conjunction with complete and meticulous subgrouping. CLINICAL SIGNIFICANCE: The identification and description of a subclustering classification system may constitute an additional step toward more precise orthodontic/orthopedic diagnosis and treatment of skeletal class III malocclusion.
OBJECTIVE: We aimed to systematically review articles investigating the efficiency of the clustering of skeletal class III malocclusion phenotypic subtypes of different ethnic origins as a diagnostic tool. METHODS: The review protocol was structured in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in Prospero (CRD42016053865). A survey of articles published up to March 2018 investigating the identification of different subgroups of skeletal class III malocclusion via cluster analysis was performed using 11 electronic databases. Any type of study design that addressed the classification of subclusters of class III malocclusion was considered. The Newcastle-Ottawa scale for cohort and cross-sectional (modified) studies was used for quality assessment. RESULTS: The final selection included 7 studies that met all the criteria for eligibility (% overall agreement 0.889, free marginal kappa 0.778). All studies identified at least 3 different types of class III clusters (ranging from 3 to 14 clusters; the total variation of the prevalence of each cluster ranged from 0.2% to 36.0%). The main shared variables used to describe the more prevalent clusters in the studies included were vertical measurements (Ar-Go-Me: 117.51°-135.8°); sagittal measurements: maxilla (SNA: 75.3°-82.95°), mandible (SNB: 77.03°-85.0°). With regard to ethnicity, a mean number of 8.5 and 3.5 clusters of class III were retrieved for Asian and Caucasian population, respectively. CONCLUSIONS: The total number of clusters identified varied from 3 to 14 to explain all the variability in the phenotype class III malocclusions. Although each extreme may be too simple or complex to facilitate an exhaustive but useful classification for clinical use, a classification system including 4 to 7 clusters may prove to be efficient for clinical use in conjunction with complete and meticulous subgrouping. CLINICAL SIGNIFICANCE: The identification and description of a subclustering classification system may constitute an additional step toward more precise orthodontic/orthopedic diagnosis and treatment of skeletal class III malocclusion.
Authors: L de Frutos-Valle; C Martin; J A Alarcón; J C Palma-Fernández; R Ortega; A Iglesias-Linares Journal: Sci Rep Date: 2020-10-21 Impact factor: 4.379