Computer-aided designed 3D-printed polymeric scaffolds for personalized reconstruction of maxillary and mandibular defects: a proof-of-concept study
Bibliographic record
Abstract
PURPOSE: To investigate the potential reconstruction of complex maxillofacial defects using computer-aided design 3D-printed polymeric scaffolds by defining the production process, simulating the surgical procedure, and explore the feasibility and reproducibility of the whole algorithm. METHODS: This a preclinical study to investigate feasibility, reproducibility and efficacy of the reconstruction algorithm proposed. It encompassed 3 phases: (1) scaffold production (CAD and 3D-printing in polylactic acid); (2) surgical simulation on cadaver heads (navigation-guided osteotomies and scaffold fixation); (3) assessment of reconstruction (bone and occlusal morphological conformance, symmetry, and mechanical stress tests). RESULTS: Six cadaver heads were dissected. Six types of defects (3 mandibular and 3 maxillary) with different degree of complexity were tested. In all case the reconstruction algorithm could be successfully completed. Bone morphological conformance was optimal while the occlusal one was slightly higher. Mechanical stress tests were good (mean value, 318.6 and 286.4 N for maxillary and mandibular defects, respectively). CONCLUSIONS: Our reconstructive algorithm was feasible and reproducible in a preclinical setting. Functional and aesthetic outcomes were satisfactory independently of the complexity of the defect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".