Journal of the World Federation of Orthodontists最新文献

Background: Class II Division 2 malocclusion presents biomechanical challenges due to retroclined maxillary incisors, often complicated by soft tissue constraints. Clear aligner therapy (CAT), though patient-friendly, shows limitations in predictably modifying labiolingual inclination. Recent attachment design advancements offer potential solutions but require biomechanical validation. Finite element modelling (FEM) serves as an effective analytical tool to evaluate these mechanics. The present study aims to compare the efficacy of three attachment geometries in expressing labiolingual inclination of maxillary incisors in a FEM model simulating Class II Division 2 malocclusion EIC.

Materials and methods: A FEM of the maxilla was generated from CBCT data of a Class II Division 2 patient. Labiolingual inclinations of 5°, 10°, 15°, 20°, and 25° were simulated on maxillary central and lateral incisors. Three attachment designs were evaluated: labial horizontal attachments (LHA), palatal horizontal attachments with labial power ridges (PHALPR) and labial-palatal reciprocal power ridges (LPRPR). Displacement and von Mises stress were calculated for teeth and supporting structures.

Results: LPRPR showed the highest displacement (0.1418 mm) and stress (384.89 MPa), followed by PHALPR (0.1211 mm; 327.35 MPa) and LHA (0.1048 mm; 204.65 MPa) respectively. Lateral incisors demonstrated greater displacement and stress than central incisors. Supporting structures showed peak stress under LPRPR conditions.

Conclusions: Attachment geometry significantly affects labiolingual inclination expression in CAT. Among the tested designs, the LPRPR configuration demonstrated superior biomechanical efficiency for managing labiolingual inclination in Class II Division 2 malocclusion.

Background: This systematic review aims to assess whether extending Alt-RAMEC (Alternating Rapid Maxillary Expansions and Constrictions) protocols from four to nine weeks yields superior skeletal, dental, and soft-tissue outcomes in growing noncleft Class III patients when compared to conventional rapid maxillary expansion (RME).

Methods: A systematic literature search was conducted across the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Scopus, Embase, Web of Science, Google Scholar, Ovid MEDLINE, EBSCOhost, and LILACS databases over the past 20 years up to 7 January 2025. Clinical trials comparing cephalometrically conventional RME with Alt-RAMEC-assisted maxillary protraction in growing noncleft Class III subjects were included. Risk of bias was assessed using the Methodological Index for Nonrandomized Studies (MINORS) and the Cochrane Risk of Bias (RoB 2) tool. A random-effects meta-analysis using RevMan was conducted for quantitative synthesis.

Results: Out of 1847 articles screened, 24 studies were included for qualitative synthesis and 11 studies for meta-analysis. The five-week Alt-RAMEC protocol showed a statistically significant improvement in the ANB angle compared to RME (mean difference 1.37°; 95% confidence intervals 0.85-1.89; P < 0.01; c² = 2.14; I² = 0%). However, both 7-week and 9-week protocols demonstrated no significant advantage over conventional RME/facemask.

Conclusions: Only the five-week Alt-RAMEC protocol offers a statistically significant but clinically negligible improvement in maxillofacial, dental, and soft-tissue outcomes over conventional RME-assisted protraction in managing growing noncleft Class III malocclusion using a Hyrax-type expander. However, this is supported by only moderate to weak evidence.

Background: The present study assessed the performance of four prominent chatbots (ChatGPT-4o, Grok 3, Gemini Advanced, and Claude 3.7 Sonnet) in answering orthodontics- and dentofacial orthopedics-related multiple-choice questions (MCQs).

Methods: One hundred MCQs were prepared by three orthodontic consultants. These questions, each with four potential answers, covered basic and advanced orthodontic knowledge and clinical case scenarios. The chatbots' answers were assessed twice at a one-week interval. Three orthodontists' answers were reported once on a Google Form. Performance was measured as the percentage of correct answers, while consistency was measured as the McNemar test and Cohen's Kappa. A P ≤ 0.05 was considered significant.

Results: The chatbots' performance ranged from 72% to 84% and from 79% to 85% in the first and second rounds, respectively. Orthodontists' performances ranged from 71% to 84%. The highest intrachatbot consistency was observed for Grok 3 (McNemar P = 1, and Kappa = 0.793), while the lowest was for Gemini Advanced and ChatGPT-4o (McNemar P = 0.001 and 0.167, respectively, and Kappa = 0.490 each). The interchatbot consistencies showed variability over time and were significantly different between Grok 3 and Claude 3.7 Sonnet (McNemar P = 0.109, and Kappa = 0.677) in the first round and between Gemini Advanced and Claude 3.7 Sonnet (McNemar P = 0.344, and Kappa = 0.647) in the second round. The consistencies among the orthodontists and between the orthodontists and chatbots were highly variable.

Conclusions: The performance of the evaluated chatbots in answering MCQs in orthodontics and dentofacial orthopedics is still below expectations, although it was slightly better than that of orthodontists. Substantial inconsistencies existed among the chatbots, thus making them unsuitable for completely replacing existing educational methods and strongly suggesting that further improvement, continuous updating, and assessments are needed.