Alveolar ridge augmentation using implants coated with recombinant human bone morphogenetic protein‐2: radiographic observations
Bibliographic record
Abstract
OBJECTIVES: Effective carrier technologies and dosing appear critical for the successful use of bone morphogenetic proteins (BMPs). This study evaluated radiographically the potential of a purpose-designed titanium porous-oxide implant surface combined with recombinant human BMP-2 (rhBMP-2) to stimulate alveolar ridge augmentation. MATERIAL AND METHODS: Twelve young-adult Labrador dogs were used. Three 10-mm titanium implants per jaw quadrant were placed 5 mm into the alveolar ridge following extraction of the premolar teeth and reduction of alveolar ridge. Six animals received implants coated with rhBMP-2 at 0.75 or 1.5 mg/ml randomized to contralateral jaw quadrants. Another six animals received implants coated with rhBMP-2 at 3 mg/ml or uncoated control using the same split-mouth design. The mucoperiosteal flaps were advanced, adapted, and sutured to submerge the implants. Radiographic registrations were made immediately postsurgery (baseline), and at weeks 4 and 8 (end of study). RESULTS: rhBMP-2-coated implants exhibited robust radiographic bone formation extending to and above the implant platform from week 4 (P<0.01). Some rhBMP-2-coated implants showed voids within the newly formed bone that gradually resolved and/or implant displacement, being severe in two animals receiving implants coated with rhBMP-2 at 3 mg/ml. Controls showed limited, if any, new bone formation at weeks 4 and 8 postsurgery. There were no significant differences among the rhBMP-2 groups in bone gain. CONCLUSIONS: The titanium porous-oxide surface serves as an effective carrier for rhBMP-2, showing a clinically significant potential to stimulate local bone formation. With the carrier technology used, therapeutic dosage appears to be in the range of 0.75-1.5 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".