Association of Plate Contouring With Hardware Complications Following Mandibular Reconstruction
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Despite considerable effort being dedicated to contouring reconstruction plates, there remains limited evidence demonstrating an association between contour and reconstructive outcomes. We sought to evaluate whether optimizing mandibular reconstruction plate contouring is associated with reduced postoperative hardware complications. STUDY DESIGN: Retrospective cohort study. METHODS: A cohort study was performed with adult patients (age ≥18 years) who underwent mandibulectomy and osseous free flap reconstruction following oncologic ablation at the University Health Network in Toronto, Canada, between January 1, 2003 and December 31, 2014. Patients with computed tomography scans performed within 1 year of reconstruction were included. Computer-based three-dimensional models were generated and used to calculate the mean plate-to-bone gap (mm). The primary outcome was plate exposure. Secondary outcome included a composite of plate exposure or intraoral dehiscence. Logistic regression models were fitted for each outcome accounting for other patient and surgical characteristics associated with the primary outcome. RESULTS: Ninety-four patients met inclusion criteria, with a mean age of 60.4 (standard deviation [SD] 14.9). The mean follow-up time was 31.4 months (range 3-94). Reconstruction was performed with fibular (57%) and scapular free flaps (43%). In the multivariable model, small mean plate-to-bone gap (<1 mm) was independently associated with 86% reduced odds of plate exposure (odds ratio [OR] 0.12; 95% confidence interval [CI] 0.02-0.55). Mean plate-to-bone gap less than 1 mm was also independently associated with reduced odds of developing a composite of plate exposure or intraoral dehiscence (OR, 0.29; 95%CI, 0.11-0.75). CONCLUSION: Optimizing plate contouring during mandibular reconstruction may decrease the development of postoperative hardware complications. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:61-66, 2022.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".