Bone regeneration at implants placed into extraction sockets of maxillary incisors in dogs
Bibliographic record
Abstract
AIM: To compare the influence of autologous or deproteinized bovine bone mineral as grafting material on healing of buccal dehiscence defects at implants installed immediately into the maxillary second incisor extraction socket in dogs. MATERIAL AND METHODS: In the maxillary second incisor sockets of 12 Labrador dogs, implants were installed immediately following tooth extraction. A standardized buccal defect was created and autologous bone particles or deproteinized bovine bone mineral were used to fill the defects. A collagen membrane was placed to cover the graft material, and the flaps were sutured to fully submerge the experimental areas. Six animals were sacrificed after 2 months, and six after 4 months of healing. Ground sections were obtained for histological evaluation. RESULTS: After 2 months of healing, all implants were osseointegrated. All buccal dehiscence defects were completely filled after 2 months irrespective of the augmentation material (autologous bone or Bio-Oss(®)) applied. Bone-to-implant contact (BIC) on the denuded implant surfaces was within a normal range of 30-40%. However, the newly formed tissue at 2 months was partially resorbed (>50% of the area measurements) after 4 months. CONCLUSIONS: Applying either autologous bone or deproteinized bovine bone mineral to dehiscences at implants installed immediately into extraction sockets resulted in high degree of regeneration of the defects with satisfactory BIC on the denuded implant surface.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".