Dental implantology and fibrous dysplasia: A 6‐year follow‐up case report and a literature review
Bibliographic record
Abstract
Abstract Background and Aim It is unclear if fibrous dysplasia (FD) represents a contraindication for implant borne rehabilitation, and only two successful cases are reported in the literature with a 4–2‐year follow‐up. The present paper discusses this issue reporting a full‐arch maxillary rehabilitation with a >5‐year follow‐up. Materials and Methods A 79‐year‐old woman complained of a progressive asymmetry of the medial facial third with inter‐arch occlusal alterations, reduced mandibular movements, upper right dental dislocation and mobility and toothache in the upper posterior jaw. The X‐ray supported monostotic FD diagnosis was followed by a remodelling intervention in general anaesthesia, with the extraction of the hopeless teeth. After that and the failed rehabilitation with a removable prosthetic device, the patient underwent implant placement procedure for a fixed ‘Toronto‐bridge’ prosthesis. Results After 6 years from implant loading, a posterior vestibular swelling of the affected maxilla was recorded, and one implant in 1.1 position had to be removed for peri‐implantitis, without compromising the rehabilitation. Conclusions The acceptable results obtained in this case should promote the dental implantology practice in FD affected jaws, evaluating the proper clinical situations and the more adequate technological solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".