ArticleCureus2023
Facial Soft Tissue Characteristics Among Sagittal and Vertical Skeletal Patterns: A Cone-Beam Computed Tomography Study.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Three-dimensional evaluation of lip changes before and after bracket debonding in patients with different vertical skeletal patterns.BMC oral health · 2026Article
- Cephalometric features associated with the mentolabial angle and lower lip eversion in young adults: A cross-sectional study.Journal of clinical and experimental dentistry · 2025Article
- Innovative Aesthetic and Functional Orthodontic Planning with Hard and Soft Tissue Analyses.Journal of clinical medicine · 2025Article
- Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review.Journal of personalized medicine · 2024Review
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Facial esthetics depend on the skeletal and dental structures underlying variable facial soft tissue thickness. In this social context, determining the relationship between external soft tissue and underlying skeletal and dental hard tissue is essential for detailed orthodontic diagnosis and treatment planning. Objective This study aims to measure facial soft tissue thickness in different sagittal and vertical skeletal patterns. Methodology This is an observational study utilizing pre-existing cone-beam computed tomography (CBCT) images of 170 subjects (110 females and 60 males) with a mean age group of 37.45 ± 13.83 years. CBCT images were then classified sagittally based on the point A-Nasion-point B (ANB) angle from Steiner's analysis into skeletal Class I, Class II, and Class III. Furthermore, vertical patterns were grouped based on the Frankfort-mandibular plane angle (FMA) from Tweed's analysis into hyperdivergent, hypodivergent, and normodivergent facial types. One-way ANOVA was used to compare the means of facial soft tissue thickness between the skeletal groups, followed by Tukey's post-hoc test for individual comparison. Multinomial logistic regression analysis was used to test the association between gender, age, and skeletal groups. The significance level was 0.05. Results One-way ANOVA revealed statistically significant differences in both sagittal and vertical groups (p≤0.05). Tukey's post hoc analysis showed that the skeletal Class III group has increased soft tissue thickness in the subnasale, upper lip, and mention compared to Class I and Class II subjects. Moreover, the hypodivergent group demonstrated increased soft tissue thickness in gnathion and mentioned landmarks in relation to the other groups. Multinomial logistic regression analysis showed significant differences between groups according to both gender and sagittal skeleton patterns (p≤0.05), with males less likely to be in Class II. Conclusions Skeletal Class III and hypodivergent groups have thicker soft tissue in specific facial landmarks. Sexual dimorphism was marked in soft tissue measurements.
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