Ridge Alterations following Immediate Implant Placement and the Treatment of Bone Defects with Bio‐Oss in an Animal Model
Bibliographic record
Abstract
BACKGROUND: Conflicting data exist on the outcome of placing Bio-Oss® (Geitslich Pharm AG, Wolhausen, Switzerland) into extraction sockets. It is therefore relevant to study whether the incorporation of Bio-Oss into extraction sockets would influence bone healing outcome at the extraction sites. PURPOSE: The aim of this study was to assess peri-implant bone changes when implants were placed in fresh extraction sockets and the remaining defects were filled with Bio-Oss particles in a canine mandible model. MATERIALS AND METHODS: Six mongrel dogs were used in the study. In one jaw quadrant of each animal, the fourth mandibular premolars were extracted with an elevation of the mucoperiosteal flap; implants were then placed in the fresh extraction sockets and the remaining defects were filled with Bio-Oss particles. After 4 months of healing, micro-computed tomography at the implant sites was performed. Osseointegration was calculated as the percent of implant surface in contact with bone. Additionally, bone height was measured in the peri-implant bone. RESULTS: Average osseointegration was 28.5% (ranged between 14.8 and 34.2%). The mean crestal bone loss was 4.7 ± 2.1 mm on the buccal aspect, 0.4 ± 0.5 mm on the mesial aspect, 0.4 ± 0.3 mm on the distal aspect, and 0.3 ± 0.4 mm on the lingual aspect. CONCLUSION: The findings from this study demonstrated that the placement of implants and Bio-Oss® particles into fresh extraction sockets resulted in significant buccal bone loss with low osseointegration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".