Medial Mandibulotomies: Is there sufficient space in the midline to allow a mandibulotomy without compromising the dentition?
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to determine the frequency of complications in median and paramedian mandibulotomies. In addition, the interdental space in the median and paramedian region was calculated. STUDY DESIGN: Retrospective study. SETTING: Tertiary care center. METHODS: A retrospective chart review was performed for all cases where a mandibulotomy was performed from 2002 to 2010. 117 charts (61 paramedian and 56 median) were identified. We included data on complications, which fell in the following 2 categories: plate and dental complications. For our second objective, we evaluated 40 different patients with base of tongue or tonsillar cancer treated with intensity modulated radiation therapy (IMRT). The interdental space between the lateral incisors and the canines was electronically calculated on the digital Panorex images. MAIN OUTCOME MEASURES: Dental and plate complications were evaluated. We also assessed interdental space. RESULTS: Patient characteristics were not significantly different. The median group had significantly more dental complications (p=0.0375, RD=0.19 and 95% CI (0.0139-0.3661)). The paramedian group had significantly more plate complications (p=0.0375, RD=0.082 and 95% CI (0.0131-0.1508). The distance between the central incisors was significantly less than the distance between the lateral incisors and canines both at the crestal and apical levels (p=0.0086 and p<0.001). CONCLUSIONS: There are significantly more dental complications in the median approach. There were significantly more plate complications in the paramedian group. In addition, there is significantly less space in the between the median region as compared to the paramedian region. This is the first study that documents the advantage of the paramedian approach for dental complications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".