Healing of BoneCeramic<sup>™</sup> at buccal dehiscence defects at implants installed immediately into extraction sockets. An experimental study in dogs
Bibliographic record
Abstract
OBJECTIVE: To assess the healing outcomes at buccal dehiscence defects after 4 months following implant placement immediately into extraction sockets (IPIES) and filled with a mixture of synthetic hydroxyl apatite (HA) 60% and β-tri-calcium phosphate (β-TCP) 40% in comparison with leaving a blood clot. MATERIAL AND METHODS: Eight Labrador dogs were used, and an implant was placed immediately following tooth extraction into the distal alveolus of the third premolars, bilaterally. Standardized buccal defects, 8 mm in depth and 4 mm in width at the coronal and 2 mm in width at the apical outlines were created. A mixture of synthetic HA 60% and β-TCP 40% was used to fill the defects at the test sites, while the control sites were left unfilled. Collagen membranes were used to cover the defects at both sides, and a non-submerged healing was allowed. After 4 months of healing, biopsies were obtained and processed for morphometric analysis. RESULTS: A vertical gain in the extent of the bony crest and of osseointegration levels of 4.2 ± 2.4 and 3.3 ± 2.1 mm at the test sites and of 5.0 ± 0.8 and 4.6 ± 1.0 mm at the control sites, respectively, were observed. BIC% within the buccal defects reached similar levels (37-42%) both at test and control sites. None of the means of the variables differed significantly between the two groups. New bone formation within the defects was higher, and the percentage of the connective tissue was lower at the control (65.7 ± 11.7% and 2.5 ± 3.3%, respectively) compared to the test sites (16.8 ± 11.3% and 48.9 ± 29.5%, respectively). These differences were statistically significant. CONCLUSIONS: The use of a mixture of synthetic HA 60% and β-TCP 40% to fill surgically created buccal dehiscence defects at IPIES sites covered with a collagen membrane did not improve osseointegration in the defect area.
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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 | 0.001 |
| 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 | 0.000 | 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".