Comparing 3D Virtual Methods for Hemimandibular Body Reconstruction
Bibliographic record
Abstract
Reconstruction of fractured, distorted, or missing parts in human skeleton presents an equal challenge in the fields of paleoanthropology, bioarcheology, forensics, and medicine. This is particularly important within the disciplines such as orthodontics and surgery, when dealing with mandibular defects due to tumors, developmental abnormalities, or trauma. In such cases, proper restorations of both form (for esthetic purposes) and function (restoration of articulation, occlusion, and mastication) are required. Several digital approaches based on three-dimensional (3D) digital modeling, computer-aided design (CAD)/computer-aided manufacturing techniques, and more recently geometric morphometric methods have been used to solve this problem. Nevertheless, comparisons among their outcomes are rarely provided. In this contribution, three methods for hemimandibular body reconstruction have been tested. Two bone defects were virtually simulated in a 3D digital model of a human hemimandible. Accordingly, 3D digital scaffolds were obtained using the mirror copy of the unaffected hemimandible (Method 1), the thin plate spline (TPS) interpolation (Method 2), and the combination between TPS and CAD techniques (Method 3). The mirror copy of the unaffected hemimandible does not provide a suitable solution for bone restoration. The combination between TPS interpolation and CAD techniques (Method 3) produces an almost perfect-fitting 3D digital model that can be used for biocompatible custom-made scaffolds generated by rapid prototyping technologies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".