Sensitivity and Specificity of Cephalometric Measures for the Diagnosis of Sagittal Skeletal Malocclusion

Objective: To evaluate and compare sensitivity and specificity of ANB, Wits, APDI and AF-BF to diagnose sagittal skeletal malocclusions, in children between 6 to 12 years old, using ROC curves, a widely accepted method for the analysis and evaluation of diagnostic tests. Material and Methods: A descriptive-comparative study of diagnostic tests was conducted. From a population of 3,000 children, a non-probabilistic sample of 209 was selected. The clinical classification of the patients as class I, II or III, made by a group of experts based on the visual inspection of models and photographs, was chosen as the gold standard. After calibration (ICC>0.94) the variables were measured in cephalograms. Eight ROC curves were plotted (I vs II, and I vs III for each one of the variables). The area under the curve was measured and compared (Ji-square test). Cut points were established. Results: To discriminate Class I from II, ANB showed the largest area under the curve (AUC) (0.876) and the cut point (best sensitivity and specificity) was at 5.75°. To discriminate class I from III, Wits showed the largest AUC (0.874) with a cut point of -3.25 mm. There were no statistical differences between the AUC for the four variables (p=0.48 y p=0.38 for class I-II and I-III). Conclusion: ANB and Wits performed better for the diagnosis of class II and III, respectively. Cut points in children were different from those reported in adults.

Craniocervical Posture in Children with Class I, II and III Skeletal Relationships

Objective: To describe the characteristics of craniocervical posture of children aged between 6 and 11 years and its relationship to their sagittal skeletal classification. Material and Methods: This descriptive cross-sectional study involved 107 children (55 girls - 52 boys), aged between 6 and 11 years. The sample included no previous orthodontically/orthopedic treated and systemically healthy children. After proper calibration, lateral skull radiographs, taken for diagnosis purpose for maxillary orthopedic treatment, were obtained by the same operator in natural head position. A radiographic analysis was made using a NEMOTEC software: 13 variables were registered: age, gender, ANB angle (to classify sagittal skeletal relationships) and 10 variables related to craniocervical posture: cervical lordosis, hyoid triangle, craniocervical angle, intervertebral spaces: C0-C1, C1-C2 and distances NSL-Ver, NLVer, ML-Ver, OPT-Hor, CVT-Hor. To evaluate the reliability of measures, 15 randomly selected radiographs were re-measured by the same investigator two weeks after the initial analysis. Results: Intra-class correlation coefficients were in a range of 0.945-0.996. Lordosis, CCA, C1-C2, OPT-Hor y CVT-Hor, values were higher in male than in female children (p<0.05). No statistically significant differences were found among groups of sagittal skeletal relationships, but class III children had a tendency to higher craniocervical flexion; 66.3% of the studied group presented rectified lordotic curvature and class II subjects presented increased values of NSL-Ver, NL-Ver and MLVer. Class I children had the lowest values for OPT-Hor and CVT-Hor. Conclusion: All craniocervical postural variables were higher in boys than in girls. No differences were found in this study between cervical postural variables with different malocclusion.

Influence of Facial and Occlusal Characteristics on Gummy Smile in Children: A Case-Control Study

Objective: To determine the association between occlusal and soft tissue characteristics with the presence of gingival smile (GS) in a pediatric population with inter-transitional mixed dentition. Material and Methods: Case-control study was performed with a probabilistic sample of 163 children in inter-transitional mixed dentition (age:8.8 years ±0.8). Cases were 37 children with GS, and controls were 126 children without GS. Occlusal variables were assessed through clinical examination, and soft tissue variables were assessed through photograms. Kappa test and intraclass correlation coefficient were done (0.87-0.96). The association between malocclusion, gender, and types of smile was assessed using a Chi square test. Comparison of quantitative variables in smile groups was made by Student t test. A multivariate binary logistic regression was performed. Results: Class II malocclusion, short upper lip at smile and short incisor clinical crown, were risk factors for gummy smile (OR= 10.4, 95%CI 3.07- 34.95, OR= 2.1, 95%CI 1.44- 3.13 and OR= 2.5 95%CI 1.34- 4.54 respectively). Lower facial height was a protective factor against GS (OR= 0.76; 95%CI 0.69- 0.85). The logistic regression model explains 48% of GS variability. Conclusion: Class II malocclusion is considered a risk factor for gummy smile. Other variables associated to gummy smile were short upper lip and short incisor clinical crown. Clinicians should considered these aspects in clinical examination of each patient to provide an adequate diagnostic and plan of treatment to control and/or correct a GS.