Regenerative Treatment of Peri‐Implantitis Bone Defects with a Combination of Autologous Bone and a Demineralized Xenogenic Bone Graft: A Series of 36 Defects
Bibliographic record
Abstract
AIM: As the treatment of peri-implantitis-induced bone loss is still a problem, we studied the regenerative treatment of these defects with a mix of autologous bone and a new type of bone graft substitute (demineralized xenogenic bone graft) including growth factors. MATERIAL AND METHODS: In a prospective manner, 36 cases of peri-implantitis-induced bone loss (depth >4 mm) in 22 patients were followed for 1 year. After resolving the acute infection by local rinsing, granulation tissue was removed. The implants were decontaminated with etching gel and the defects were filled with autologous bone mixed 1:1 with a xenogenic bone graft. The prosthetic reconstructions did not have to be removed. Values of probing depths as well as bone defects were analyzed. RESULTS: The radiologic evaluation of the bone defects after regenerative treatment revealed a mean reduction of 3.5 mm comparing the values from 5.1 mm prior to surgery to 1.6 mm 1 year after treatment. Average reduction of the probing depth was 4 mm. The remaining bone defects were larger than 3 mm in 4 out of 36 implants 1 year after treatment. Probing depths of more than 4 mm were present in seven implants. CONCLUSION: Within the limits of the study, we conclude that for bone defects larger than 4 mm in case of peri-implantitis, this single surgical intervention provided a reliable method to reduce bone defects.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".