Tissue Engineering with Recombinant Human Bone Morphogenetic Protein‐2 for Alveolar Augmentation and Oral Implant Osseointegration: Experimental Observations and Clinical Perspectives*
Bibliographic record
Abstract
Surgical placement of oral implants is governed by the prosthetic design and by the morphology and quality of the alveolar bone. Nevertheless implant placement often appears difficult, if at all possible, due to aberrations of the alveolar ridge. Hence prosthetically dictated implant positioning often entails augmentation of the alveolar ridge and adjoining structures. In this review we discuss recent observations of the biologic potential, clinical relevance, and perspectives of application of recombinant human bone morphogenetic protein-2 (rhBMP-2) technologies for alveolar bone augmentation and oral implant osseointegration. Using discriminating critical-size supraalveolar defects and clinical modeling in dogs, we show that rhBMP-2 has a substantial potential for augmenting alveolar bone and supporting osseointegration of titanium oral implants. Moreover, using clinical modeling, we demonstrate re-osseointegration in advanced periimplantitis defects and long-term functional loading of titanium oral implants placed into rhBMP-2-induced bone. Our studies suggest that inclusion of rhBMP-2 for alveolar bone augmentation and oral implant fixation will not only enhance the predictability of existing clinical protocol but also allow new approaches to these procedures.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".