Implant-Retained Prosthetic Reconstruction of Complicated Maxilla and Mandibulla Defect
Bibliographic record
Abstract
Patients with severe maxillary or mandibular defects have serious functional problems with mastication, swallowing, speech and appearance. Therefore, to restore function and improve quality of life, the lost tissue must be replaced. Reconstruction can be accomplished surgically, with autogenous tissue replacements, or prosthetically while a combined treatment protocol is required in complicated cases. A 26-year-old male patient was referred to Gazi University Faculty of Dentistry, Department of Prosthodontics. Clinical and radiographical examination and anamnesis reveal that the anterior maxilla and anterior mandible are destroyed with teeth, alveolar bone, and surrounding soft tissues at a gunshot injury. The bone defects were reconstructed with fibula bone grafts and soft tissue losses were replaced with free flaps. Four implants were placed in each upper and lower jaw. After digitalizing the impressions, maxillary and mandibular prosthetic restorations were designed using the Exocad software. This design was produced from resin on a 3D printer and tried on the patient. Considering the existing implant positions and desired teeth positions, a screw-retained metal-ceramic fixed prosthesis for the upper jaw and a Toronto bridge for the lower jaw were constructed. Prosthetic restorations of maxillary or mandibular defects should be individually designed considering the extent of the defect, remaining tissue support, and existing dentition. Dental implants are generally an important part of the treatment in these cases. For prosthetic reconstruction of maxillary and mandibular defects, dental implants provide many advantages. Suboptimal implant positions in reconstructed jaws can be compensated by individually designed prosthetic restorations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".