Influence of various implant platform configurations on peri-implant tissue dimensions: an experimental study in dog
Bibliographic record
Abstract
AIM: To evaluate the influence (i) of various implant platform configurations and (ii) of implant surface characteristics on peri-implant tissue dimensions in a dog model. MATERIAL AND METHODS: Mandibular premolars and first molars were extracted bilaterally in six Labrador dogs. After 3 months of healing, two implants, one with a turned and a second with a moderately rough surface, were installed on each side of the mandible in the premolar region. On the right side of the mandible, implants with a tapered and enlarged platform were used, while standard cylindrical implants were installed in the left side of the mandible. Abutments with the diameter of the cylindrical implants were used resulting in a mismatch of 0.25 mm at the tapered implant sites. The flaps were sutured to allow a non-submerged healing. After 4 months, the animals were sacrificed and ground sections were obtained for histometric assessment. RESULTS: All implants were completely osseointegrated. A minimal buccal bone resorption was observed for both implant configurations and surface topographies. Considering the animals as the statistical unit, no significant differences were found at the buccal aspect in relation to bone levels and soft tissue dimensions. The surface topographies did not influence the outcomes either. CONCLUSIONS: The present study failed to show differences in peri-implant tissue dimensions when a mismatch of 0.25 mm from a tapered platform to an abutment was applied. The surface topographies influence a neither marginal bone resorption or peri-implant soft tissue dimension.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".