Osteogenesis at implants without primary bone contact – An experimental study in dogs
Bibliographic record
Abstract
AIM: To evaluate the healing at implants with a moderately rough surface placed and stabilized in recipient sites of dimensions deeper and larger than that of the implants to avoid any contact between parent bone and the implant. MATERIAL & METHODS: In six Labrador dogs, premolars and first molars were extracted bilaterally in the mandible. After 3 months of healing, mucoperiosteal full-thickness flaps were elevated and the premolar area of the alveolar bony crest was selected. Three recipient sites were prepared to place three implants. One implant was used as control. The other two were placed in recipient sites which left a circumferentially and periapical prepared defect of 0.7 mm (small) and 1.2 mm (large), respectively. All implants were stabilized with passive fixation plates to maintain the implants stable and without any contact with the implant bed. After 3 months of submerged healing, the animals were sacrificed. Ground sections were prepared and analyzed histomorphometrically. RESULTS: The BIC% was 5.3% and 0.3% for implants placed in small and large defect sites, respectively, whereas it was 46.1% for control implants. The differences were statistically significant. The width of the residual defects was 0.4 and 0.5 mm at the small and large defects, respectively. An approximately 0.09 mm layer of dense connective tissue (DCT) rich in fibers and fibroblast-like cells was observed adherent to the implant surfaces. The percentage of implant surface covered by DCT was 92.8% and 95.6% at the small and large defects, respectively. CONCLUSION: Osseointegration was observed at the test sites, and the dimensions of the defects influenced the outcomes. However, the degree of osseointegration at both small and large defects was very low compared with the control sites.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".