Clinical application of autogenous demineralized dentin matrix loaded with recombinant human bone morphogenetic‐2 for socket preservation: A case series
Bibliographic record
Abstract
BACKGROUND: Demineralized dentin matrix (DDM) has potential application as a carrier for recombinant human bone morphogenetic protein-2 (rhBMP-2) in bone regeneration. PURPOSE: To evaluate the efficacy of DDM loaded with rhBMP-2 for socket preservation. MATERIALS AND METHODS: DDM loaded with rhBMP-2 (DDM/rhBMP-2) was applied to 10 experimental sites and DDM alone to 6 control sites. The changes in height and width of the extraction socket after preservation were measured by cone beam computed tomography. Trephine cores were harvested for histomorphometric evaluation before placement of the implant. RESULTS: The reductions in height and width of the socket were more significant in the group treated with DDM than in the group treated with DDM/rhBMP-2. The amount of new bone formation was 34.39% with DDM/rhBMP-2 and 29.75% with DDM; the respective amounts of residual dentin were 8.35% and 16.15%. Although the differences were not statistically significant, the dimensional changes, amount of bone formation, and replacement of DDM in DDM/rhBMP-2 with bone were superior to those of DDM alone. CONCLUSIONS: Within the limitations of this study, we suggest that DDM may be a potential carrier for rhBMP-2 and that it may be possible to reduce the rhBMP-2 concentration to 0.2 mg/mL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".