The Influence of Surface Roughness on the Displacement of Osteogenic Bone Particles during Placement of Titanium Screw-Type Implants
Bibliographic record
Abstract
BACKGROUND: Previously, we demonstrated that bone debris, which is translocated during dental implant placement, has osteogenic potential. Therefore, it was hypothesized that implant surface roughness can influence the amount of translocated bone debris/particles and thereby the osteogenic response. MATERIAL AND METHODS: Small titanium implants were left turned (smooth) or blasted and acid etched. The implants were placed in fresh cadaver bone. After explantation, the implants were incubated in a culture medium containing β-glycerophosphate and dexamethasone up to 24 days. Subsequently, histology, scanning electron microscopy (SEM), DNA analysis, and calcium (Ca) content measurements were performed. RESULTS: For both types of implant during implant placement, bone particles were translocated because of inherent roughness of the implant. SEM and histology confirmed the presence of a bone-like tissue on the surface of both types of implants, as also confirmed by DNA and Ca measurements. However, the significantly higher roughness of the etched implants accounted for more bone debris and accordingly elevated osteogenic response. Control samples, which had not been placed into bone, did not show mineralization in the same medium. CONCLUSION: The present study, for the first time, demonstrated that implant surface roughness can increase the amount of the translocated bone particles and thereby also have a beneficial effect on the osteogenic response of these bone particles. It is hypothesized that these bone fragments behave like miniature auto-grafts and thereby play a significant role to enhance peri-implant osteogenesis. Optimization of surface topography should be evaluated to take advantage of this additional effect of surface roughness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".