Predictors of favorable soft tissue profile outcomes following Class II Twin-block treatment

• 발행기관: 대한치과교정학회
• 저자명: JiEun Kim SuJung Mah TaeWoo Kim SuJung Kim KiHo Park YoonGoo Kang
• 간행물 정보: 『The Korean Journal of Orthodontics』제48권 제1호, 11~22쪽, 전체 12쪽
• 주제분류: 의약학 > 기타의약학
• 파일형태: PDF
• 발행일자: 2018.01.31

영문 초록

Objective: The aim of this study was to determine cephalometric factors that help predict favorable soft-tissue profile outcomes following treatment with the Class II Twin-block appliance. Methods: Pre- and post-treatment lateral cephalograms of 45 patients treated with the Class II Twin-block appliance were retrospectively analyzed. Profile silhouettes were drawn from the cephalograms and evaluated by three orthodontists in order to determine the extent of improvement. Samples were divided into a favorable group (upper 30% of visual analogue scale [VAS] scores, n = 14) and an unfavorable group (lower 30% of VAS scores, n = 14). Skeletal and soft-tissue measurements were performed on the cephalograms and an intergroup comparison was conducted. Results: An independent t -test revealed that the following pre-treatment values were lower in the favorable group compared to the unfavorable group: lower incisor to mandibular plane angle, lower incisor to pogonion distance, point A-nasionpoint B angle, sella-nasion line (SN) to maxillary plane angle, SN to mandibular plane angle, gonial angle, and symphysis inclination. The favorable group had a larger incisor inclination to occlusal plane. Moreover, the favorable group showed larger post-treatment changes in gonial angle, B point projection, and pogonion projection than did the unfavorable group. Conclusions: Class II malocclusion patients with a low divergent skeletal pattern and reduced lower incisor protrusions are likely to show more improvement in soft-tissue profile outcomes following Class II Twin-block treatment.

목차

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

CONCLUSION

REFERENCES

키워드

Class II Twin-block Soft-tissue profile Cephalometric predictors