Bone Formation in a Local Defect around Dental Implants Coated with Extracellular Matrix Components
Bibliographic record
Abstract
PURPOSE: The coating of implant surfaces with components of the extracellular matrix offers an approach to influence peri-implant bone healing. In this study, bone healing around coated implants is analyzed in a peri-implant defect model. MATERIALS AND METHODS: Eight months after extraction of the premolar teeth, six dogs received 48 implants (eight per animal) in the mandible. Implant surfaces were sandblasted and acid-etched, and some were additionally coated with collagen type II and chondroitin sulfate (collagen/CS). On each side of the mandible, implants either had no peri-implant defect (control side) or a vertical defect of 5 mm in depth and 0.5, 1.0, or 2.0 mm in width. Implants healed submerged for 8 weeks. Fluorochrome staining, histology, and histomorphometry were used to analyze implant osseointegration. RESULTS: Fluorochrome labels showed an increased mineralization around collagen/CS-coated surfaces at 4 weeks (p = .031). Histomorphometry generally showed lower vertical and horizontal bone apposition with increasing gap size for both surface types. In gapless sites and 0.5-mm gaps, collagen/CS coated implants showed increased bone volume in areas directly adjacent to the implant, in comparison with uncoated implants (p < .05). CONCLUSION: The width of the peri-implant gap influences peri-implant bone formation. Complete filling of all gaps by newly formed bone could not be observed around either surface. In proximity to the surface, implant surface coating by collagen/CS positively influenced bone formation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".