Vertical Osteoconductive Characteristics of Titanium Implants with Calcium‐Phosphate‐Coated Surfaces – A Pilot Study in Rabbits
Bibliographic record
Abstract
INTRODUCTION: Osteoconductive characteristics of different implant surface coatings are in the focus of current interest. The aim of the present study was to compare the vertical osteoconductivity at the implant shoulder of supracrestal inserted calcium-phosphate coated implants (SLA-CaP) with conventional sand-blasted/acid-etched (SLA) implants in a rabbit model. MATERIALS AND METHODS: SLA-CaP and SLA implants were inserted bilaterally in the mandible of four rabbits in a split-mouth design. The implants were placed 2 mm supracrestal. After 3 weeks, at the left and right implant shoulder, the percentage of linear bone fill (PLF) as well as bone-implant contact (BIC-D) were determined. RESULTS: After 3 weeks, newly formed woven bone could be found at the shoulder of the most of both surface-treated implants (75%). PLF was significantly higher in SLA-CaP implants (11.2% vs. 46.5%; n = 8, p = .008). BIC-D was significantly increased in the SLA-CaP implants (13.0% vs. 71.4%; n = 8, p < .001) as well. CONCLUSION: The results of this study show for the first time that calcium-phosphate coated surfaces on supracrestal inserted implants have vertical osteoconductive characteristics and increase the bone-implant contact at the implant shoulder significantly in a rabbit model. In clinical long-term settings, these implants may contribute to a better vertical bone height.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".