Peripheral giant cell granuloma associated with dental implants: Case‐series
Bibliographic record
Abstract
PURPOSE: The objectives were to characterize clinico-pathologically a large series of peri-implant peripheral giant cell granuloma (PGCG), and investigate the role of foreign material as a possible etiological factor. MATERIAL AND METHODS: The study was retrospective, conducted on peri-implant specimens submitted for histology between 2005 and 2021. RESULTS: Three hundred and thirty-five peri-implant biopsies were retrieved, of which 52 (15.5%) were PGCG. The study population included 28 females and 24 males, age 35-92 years, mean 61. 51.2% reported bone involvement. The lesion involved the margins of the specimen in 65.3%, recurrence was reported in 46.1%. In 58.8% the implant was removed at the same time the specimen was submitted for histopathological analysis. Small foci of black granular foreign material were observed in 53.8% of cases of which 67.8% were birefringent under polarized light. The foreign material granules were not ingested inside multinucleated giant cells, but were scattered in the stromal compartment. CONCLUSIONS: Peri-implant PGCG is locally aggressive, with frequent bone involvement and high recurrence rate, resulting in implant loss in the majority of cases. The high recurrence rate may be related to conservative or inadequate surgery. Foreign material although common does not seem to have a role in its development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".