Does Surgery-First Orthognathic Approach Improve Quality of Life of Orthodontic Patients With Skeletal Class III Malocclusion? A Systematic Review Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Guidelines
Bibliographic record
Abstract
The surgery-first approach has gained popularity in recent years due to its ability to bypass the presurgical orthodontic phase and significantly reduce treatment duration. However, its broader impact on quality of life and psychosocial outcomes in patients with skeletal class III malocclusion has not been systematically evaluated. This systematic review aimed to assess the effects of the surgery-first approach on quality of life, psychosocial outcomes, and treatment duration in this patient population. The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A comprehensive search of PubMed, Cochrane Library, Scopus, Web of Science, and Embase databases identified studies evaluating the surgery-first approach in skeletal class III patients. Studies addressing the impact of this approach on quality of life, psychosocial outcomes, and treatment duration were included, and the quality of evidence was assessed using the Newcastle-Ottawa Scale. Due to heterogeneity in study designs and outcomes, a narrative synthesis of the data was performed. A total of eight studies, conducted between 2015 and 2022, with 252 participants, met the inclusion criteria. The surgery-first approach demonstrated significant improvements in quality of life, particularly in the early postoperative stages, while avoiding the decline typically observed during the presurgical phase of the conventional orthodontics-first approach. Psychosocial benefits, such as reduced anxiety and enhanced self-esteem, were consistently reported. Treatment duration for the surgery-first approach ranged from 7 to 15 months, significantly shorter than the conventional approach. However, the strength of the evidence was limited by small sample sizes and a lack of randomization in most studies. The surgery-first approach offers distinct advantages for skeletal class III patients, including improved quality of life, enhanced psychosocial outcomes, and shorter treatment duration compared to the conventional orthodontics-first protocol. Nevertheless, further high-quality randomized clinical studies with longer follow-up periods are needed to evaluate long-term outcomes and ensure treatment stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".