Recent Outcomes and Perspectives of the Application of Bone Morphogenetic Proteins in Implant Dentistry
Bibliographic record
Abstract
BACKGROUND: Since the discovery of bone morphogenetic proteins (BMPs), the number of related studies has increased substantially, and more recent outcomes have cast encouraging perspectives on their use in reconstructive surgery. PURPOSE: The aim of the present review was to summarize the present knowledge about the use of BMPs in conjunction with dental implants based on the literature. MATERIALS AND METHODS: Scientific articles dealing with the use of growth factors and bone healing with or without dental implants were searched for on MEDLINE and critically scrutinized. RESULTS: Thirty-nine scientific reports formed the base for the present review. Whereas the osteoinductive capability of BMPs is well documented, studies on their effects in implant dentistry are still incipient. Preclinical and clinical studies did not show outstandingly good outcomes of the application of BMPs compared with conventional treatments or controls. CONCLUSIONS: The number of studies in the field of dental implantology in which BMPs have been used is still too small for establishing clinical protocols of their use in order to improve a recipient bone bed prior to implant placement or to enhance the integration process of an implant.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".