Auricular Rehabilitation by Means of Bone Grafting from the Iliac Crest in Combination with Porous Extraoral Implants: A Case Report
Bibliographic record
Abstract
BACKGROUND: Maxillofacial defects caused by cancer treatment are a huge problem affecting the quality of life of patients. Some of these deformities are minimized using facial epitheses, which need some additional retention devices like glasses or skin adhesives. The use of extraoral fixtures as bone anchorage was introduced many years ago and since then many patients were rehabilitated with better results. PURPOSE: Because of poor bone conditions, for example, irradiated bone, the success rate of extraoral implants is less than in the oral cavity, causing difficulties to rehabilitation. One possible cause of fixture failure could be the poor primary stability achieved in some cases, hence, with an increased bone contact implant stability and survival could be improved. The present report discusses possibilities to use extraoral fixtures with a modified surface structure. MATERIALS AND METHODS: A new porous surfaced Brazilian extraoral implant (MasterExtra, Conexão, Sistema de Próteses, São Paulo, Brazil) was used. A bone transplant from the iliac crest was taken to make it possible to insert at least three extraoral implants for an auricle epithesis. Clinical evaluation and resonance frequency analysis (RFA) measurements were performed during the course of the treatment. RESULTS: Eight months after grafting, four fixtures were inserted. Three fixtures were used for connection of an auricular epithesis. RFA measurements did show high initial values and the values remained stable during the course of the treatment and at later checkups. CONCLUSION: Porous fixture is a good option in areas where the bone is compromised. RFA is a good tool also in the clinical setting to evaluate immediate and long-term stability of extraoral fixtures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".