Five-Segment Mandibular Osteotomy: A Novel Approach to the Correction of Complex Post-Traumatic Mandibular Deformities
Bibliographic record
Abstract
BACKGROUND: Post-traumatic mandibular malocclusion is a complex condition that poses a significant challenge to reconstructive surgeons. The malocclusion that ensues from bilateral condylar and parasymphseal fractures presents a particular challenge as it leads to bilateral posterior shortening and lingual tilting of dental arch leading to a combination of open anterior bite, crossbite, overbite, underbite, and/or facial asymmetry. The complexity of such malocclusion requires intricate freedom of movement of the mandibular arch that can be achieved by performing a 5-segment mandibular osteotomy. METHOD: This is a case series of 9 adult patients with significant post-traumatic mandibular malocclusion who were treated with 5-segment mandibular osteotomy technique. This article details the demographics, surgical technique and outcomes in this cohort of patients. RESULTS: All 9 patients in this series had condylar fracture as part of the index mandibular trauma. They have a common post-traumatic deformity of the mandibular arch due to shortening of the vertical mandibular height in the fracture site and variable degrees of lingual tilting leading to crossbite. The 5-segment mandibular osteotomy provided an adequate correction of dental and facial deformities in all 9 patients. One patient had a relapse of the dental malocclusion that required postoperative rescue orthodontics. Furthermore, one patient had a significant postoperative hemorrhage that required a facial artery ligation. CONCLUSION: Post-traumatic mandibular malocclusion is a complex deformity that poses a great challenge to practicing surgeons. Five-segment mandibular osteotomy is a technique that provides ample degrees of movement of mandibular segments that is necessary to correct such deformity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".