Scapula or Fibula?: Prospective data-driven decision criteria for flap selection in mandibular reconstruction planning
Bibliographic record
Abstract
OBJECTIVES: The objectives of the study are twofold: 1) to propose a set of criteria for virtually evaluating different types of reconstructions for mandibular defects during surgical planning, and 2) to prospectively apply the criteria to a series of mandibular reconstructions to select the optimal flap. MATERIALS AND METHODS: Clinically relevant assessment criteria were selected, including volumetric overlap, Haussdorf-95, bony contact, ramus/symphysis angles, and dental implantability. In 2021, 21 consecutive patients undergoing mandibular reconstructions were consented to the study. For each patient, seven virtual reconstructions were created: vertical scapula, horizontal scapula, 1-segment fibula, optimal fibula determined by the Ramer-Douglas-Peucker (RDP) algorithm, RDP + 1 fibula, and RDP-1 fibula. The surgeon selected the optimal reconstruction for each patient based on the objective criteria and clinical considerations. RESULTS: The vertical scapula was selected for 12 cases, the RDP fibula for 6 cases, and the horizontal scapula for 3 cases. For defects involving the symphysis, the horizontal scapula was the frequently selected as its geometry could be leveraged to recreate the symphysis angle. For the remaining defects, the RDP fibula optimizes the bony contact, performs well in volume overlap, and is the most implantable. The vertical scapula minimizes osteotomies while maximizing each criterion but results in low implantability. On average, the chosen reconstruction performed better on all criteria compared to the remaining proposed models. CONCLUSION: The criteria proposed comprises of measurable and clinically important metrics that can serve as a useful tool for reconstructive surgeons in selecting the optimal flap for mandibular reconstruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".