Success Rates of Different Traction Methods for Impacted Canines
Year: 2026
Type: article
Study design: Systematic Review
Abstract: AIM: To compare the clinical success rates, biomechanical efficiency and biological safety of orthodontic traction methods for impacted maxillary canines by synthesising available clinical and finite element evidence to inform optimal treatment selection. METHODS: A systematic literature review of PubMed was performed for clinical and finite element method (FEM)-based studies published from 2000 to 2025. Inclusion criteria required quantitative data on eruption success, force application, root resorption and treatment duration. Sixteen studies met the criteria and were analysed using thematic synthesis and comparative tables. RESULTS: Traction methods using temporary anchorage devices (TADs), TMA cantilevers, corticotomy-assisted protocols and double-wire techniques demonstrated consistently high success rates (up to 100\%) and superior biomechanical control. These approaches were associated with reduced root resorption, improved anchorage stability and shorter treatment duration. Conversely, Kilroy springs and clear aligners exhibited biomechanical limitations and generated elevated stress on adjacent teeth. Patient factors, notably age, impaction depth and compliance, were significant predictors of outcome. FEM studies supported the mechanical advantages of TAD-based and corticotomy-assisted strategies when combined with three-dimensional planning. CONCLUSION: Individually tailored traction strategies informed by CBCT imaging and biomechanical planning achieve the most favourable outcomes. TAD-based systems and corticotomy-assisted approaches are particularly effective in adult or complex cases. Further randomised controlled trials using standardised force systems and outcome metrics are needed to strengthen clinical guidelines for impacted canine management.
Author: Khunpradit, Chotwit
Language: English
Subfield: Orthodontics
Field: Dentistry
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7131074263
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