Comparison of Maxillary Distraction Osteogenesis and Conventional Orthognathic Osteotomy: A Systematic Review
Bibliographic record
Abstract
Maxillary hypoplasia, affecting 0.3% of the US population and nearly 25% of patients with cleft lip and/or palate (CLP), often results in Class III malocclusion with significant functional and esthetic challenges. Treatment options include LeFort I distraction osteogenesis (DO) and conventional osteotomy (CO), but reported outcomes vary widely. A systematic review of PubMed, Embase, Scopus, and CINAHL identified 17 studies (6 randomized controlled trials, 11 retrospective cohort studies) from 5076 screened. Outcomes assessed included skeletal relapse, speech changes, velopharyngeal insufficiency (VPI), soft tissue adaptations, and complications. Study quality was evaluated using the Newcastle-Ottawa scale and Cochrane risk-of-bias tool. Findings showed no significant differences in skeletal relapse between DO and CO. Speech and VPI outcomes were comparable, with deterioration in 10% to 45% of DO patients and 22.2% to 81.8% of CO patients. DO provide superior soft tissue improvements, particularly in nasal and lip landmarks. Complication rates ranged from 5% to 20% for DO and 21% to 22.2% for CO. Overall, evidence comparing DO and CO remains limited and inconsistent, preventing definitive conclusions on skeletal stability, speech outcomes, VPI risk, and complications. While DO may offer better soft tissue outcomes, it shares high complication rates and patient dissatisfaction. Further research is essential.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".