Implant Osseointegration in Circumferential Bone Defects Treated with Latex‐Derived Proteins or Autogenous Bone in Dog's Mandible
Bibliographic record
Abstract
BACKGROUND: In sites with diminished bone volume, the osseointegration of dental implants can be compromised. Innovative biomaterials have been developed to aid successful osseointegration outcomes. PURPOSE: The aim of this study was to evaluate the osteogenic potential of angiogenic latex proteins for improved bone formation and osseointegration of dental implants. MATERIALS AND METHODS: Ten dogs were submitted to bilateral circumferential defects (5.0 × 6.3 mm) in the mandible. Dental implant (3.3 × 10.0 mm, TiUnite MK3™, Nobel Biocare AB, Göteborg, Sweden) was installed in the center of the defects. The gap was filled either with coagulum (Cg), autogenous bone graft (BG), or latex angiogenic proteins pool (LPP). Five animals were sacrificed after 4 weeks and 12 weeks, respectively. Implant stability was evaluated using resonance frequency analysis (Osstell Mentor, Osstell AB, Göteborg, Sweden), and bone formation was analyzed by histological and histometric analysis. RESULTS: LPP showed bone regeneration similar to BG and Cg at 4 weeks and 12 weeks, respectively (p ≥ .05). Bone formation, osseointegration, and implant stability improved significantly from 4 to 12 weeks (p ≤ .05). CONCLUSION: Based on methodological limitations of this study, Cg alone delivers higher bone formation in the defect as compared with BG at 12 weeks; compared with Cg and BG, the treatment with LPP exhibits no advantage in terms of osteogenic potential in this experimental model, although overall osseointegration was not affected by the treatments employed in this study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".