Evaluation of Guided Bone Regeneration around Oral Implants over Different Healing Times Using Two Different Bovine Bone Materials: A Randomized, Controlled Clinical and Histological Investigation
Bibliographic record
Abstract
PURPOSE: To evaluate the potential of two bone substitute materials and the influence of different healing periods in guided bone regeneration therapy of osseous defects around implants. MATERIALS AND METHODS: Twenty-four edentulous patients received implants in the region of the lost lower incisors. Around two standardized osseous defects were created, treated either with a 50:50 mixture of PepGen P-15® and OsteoGraf®/N-700 (test group) or with BioOss® (control group), and covered with titanium membranes. After healing periods of 2, 4, 6, or 9 months, the implants were removed together with the surrounding bone and subsequently prepared for histological evaluations. RESULTS: Defect depths in both groups showed a clinical reduction after intervention. The histologically measured distance from the implant shoulder to the first point of bone-implant contact (BIC) after treatment did not differ between the two groups. The healing time influenced the level of the first point of BIC, with a longer healing period producing a more coronal first point of BIC. A greater percentage BIC and a higher fraction of mineralized bone were found in the pristine bone area compared with the augmented defect area. CONCLUSION: It can be concluded that in the treatment of osseous defects around oral implants, both materials were equally effective bone substitute materials when used in combination with guided bone regeneration.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".