Reconstruction of the Anterior Maxilla with Platelet Gel, Autogenous Bone, and Titanium Mesh: A Case Report
Bibliographic record
Abstract
BACKGROUND: Reconstruction of defects in the anterior part of the maxilla to enable implant placement is a challenging treatment. Recent studies have suggested that the use of autogenous platelet gel may contribute to improved healing of bone grafts. PURPOSE: A case is presented in which particulated autogenous bone, platelet gel, and a titanium mesh were used for alveolar bone reconstruction of the anterior maxilla prior to implant placement. MATERIALS AND METHODS: Corticocancellous bone from the iliac crest was mixed with a preparation of autogenous platelet gel (platelet-rich plasma, thrombin, and calcium chloride) and placed against a titanium mesh fixed to the bone of the palate in a patient with severe resorption of the anterior maxilla. After 4.5 months of healing the mesh was removed and titanium implants were placed. A prolonged healing period of 8 months was allowed before healing abutments were placed and a fixed dental bridge was fabricated. RESULTS: Healing was uneventful, and the anterior maxilla had increased in height and width during the initial healing. All implants became integrated and have been supporting a fixed dental bridge for over 3 years with no dramatic dimensional changes of the graft. CONCLUSIONS: This case demonstrates that particulated autogenous bone and platelet gel may be used for reconstruction of the anterior maxilla. Autogenous growth factors in the gel possibly contributed to the positive outcome. Controlled clinical studies are needed to evaluate the effect of using platelet-rich plasma.
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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) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".