The Importance of Histopathological Diagnosis in the Management of Lesions Presenting as Peri‐Implantitis
Bibliographic record
Abstract
PURPOSE: This study is a histopathological analysis of lesions clinically diagnosed as peri-implantitis (PI). MATERIALS AND METHODS: This retrospective study included microscopic findings in 117 peri-implant biopsies from lesions presenting clinical and radiographic features of peri-implantitis. RESULTS: The study group included 117 biopsies, mean age 55.2 years; 60.9% of biopsies were from failing implants during explantation, the remaining from surviving implants. All cases showed microscopic evidence for inflammation; however, although 41% exhibited only nonspecific inflammation, 29.9% exhibited actinomyces-related inflammation, 18.8% pyogenic granuloma (PG), and 10.3% giant cell granuloma (GCG). Differences in implant failure rates between pathological diagnostic groups were not statistically significant. Lesions with simple inflammation could not be distinguished clinically or radiographically from the potentially destructive lesions. CONCLUSIONS: There were no clinical features which could distinguish PI with simple inflammation from potentially destructive lesions mimicking PI, such as GCG, PG, and actinomycosis. However, to control GCG and PG surgical procedures would be recommended, actinomycosis would indicate specific antibiotics, whereas in nonspecific inflammation, these measures may not be indicated. The results of the present study provide evidence for the importance of early microscopic examination of lesions presenting clinically as peri-implantitis, a step toward more accurate diagnosis and improved treatment of PI and lesions mimicking PI.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".