Sequential Healing at Implants with Different Configuration and Modified Surfaces: An Experimental Study in the Dog
Bibliographic record
Abstract
PURPOSE: To evaluate the peri-implant soft and hard tissue adaptation at implants with different modified surfaces and configurations. MATERIALS AND METHODS: Six Beagle dogs were used. Mandibular premolars and first molars were extracted bilaterally. After 3 months, full-thickness flaps were elevated, and two different types of trans-mucosal implants (ICX-Gold®, Medentis Medical GmbH, Dernau, Germany and SLActive®, Institute Straumann, Bern, Switzerland) and two different surfaces were randomly installed in the distal regions of one side of the mandible. Abutments were applied, and a nonsubmerged healing was allowed. After 1 month, the procedures were performed in the other side of the mandible, and after a further month, the animals were sacrificed, biopsies were collected, and ground sections prepared for histological examination. RESULTS: Similar results in marginal bone and soft tissues dimensions were observed after 1 month of healing at the two implant systems used, and no major changes could be observed after 2 months of healing. After 1 month, the percentage of new bone was 69.0% and 68.8% at ICX-Gold and SLActive surfaces, respectively. After 2 months, the percentage of new bone was 67.8% and 71.9% at ICX-Gold Medentis and SLActive surfaces, respectively. No statistically significant differences in osseointegration were found. CONCLUSION: The two implant systems used resulted in similar osseointegration after 1 and 2 months of healing.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".