Surgical Reconstruction of Peri‐Implant Bone Defects with Prehydrated and Collagenated Porcine Bone and Collagen Barriers: Case Presentations
Bibliographic record
Abstract
BACKGROUND: Surgical reconstruction of peri-implant defects is challenging and unpredictable due to, for example, the extent of the bone defect or the osteogenic potential of adjunctive materials used. PURPOSE: To study the healing capacity of a new bone xenograft material in the treatment of peri-implant defects. MATERIAL AND METHODS: In three cases with advanced peri-implant defects, flap surgery was performed. After thorough debridement including cleaning of the exposed implant surface, prehydrated and collagenated porcine bone (PCPB) particles were placed into the defect. A bioresorbable collagen barrier was adapted and placed over the defect and the flaps were relocated. After 6 and 12 months of healing, clinical and radiographic examinations were done. In one case, the surgical procedure was repeated 6 months postoperatively. One year after the second surgery, a bone biopsy was harvested and analyzed with histology. RESULTS: All defects healed uneventfully. At 6 months, probing depths were reduced by 3-4 mm with no bleeding on probing or pus formation. At 12 months, healthy peri-implant conditions were found. Intra-oral radiographs showed gain of the marginal bone level by 2-4 mm. In the case where reconstructive surgery was repeated, histology showed osteoconductive properties as bone formation with typical osteoblastic seams was observed directly on the surface of the grafted particles. CONCLUSION: The presented cases show that PCPB have favorable properties enhancing bone regeneration in peri-implant bone defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".