Influence of implant positioning in extraction sockets on osseointegration: histomorphometric analyses in dogs
Bibliographic record
Abstract
AIM: To evaluate the influence of implant positioning into extraction sockets on osseointegration. MATERIAL AND METHODS: Implants were installed immediately into extraction sockets in the mandibles of six Labrador dogs. In the control sites, the implants were positioned in the center of the alveolus, while in the test sites, the implants were positioned 0.8 mm deeper and more lingually. After 4 months of healing, the resorptive patterns of the alveolar crest were evaluated histomorphometrically. RESULTS: All implants were integrated in mineralized bone, mainly composed of mature lamellar bone. The alveolar crest underwent resorption at the control as well as at the test sites. After 4 months of healing, at the buccal aspects of the control and test sites, the location of the implant rough/smooth limit to the alveolar crest was 2 + or - 0.9 mm and 0.6 + or - 0.9 mm, respectively (P<0.05). At the lingual aspect, the bony crest was located 0.4 mm apically and 0.2 mm coronally to the implant rough/smooth limit at the control and test sites, respectively (NS). CONCLUSIONS: From a clinical point of view, implants installed into extraction sockets should be positioned approximately 1 mm deeper than the level of the buccal alveolar crest and in a lingual position in relation to the center of the alveolus in order to reduce or eliminate the exposure above the alveolar crest of the endosseous (rough) portion of the implant.
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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 | 0.000 |
| 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".