The Effects of Titanium Implant Surface Topography on Osseointegration: Literature Review
Bibliographic record
Abstract
Background: A variety of claims are made regarding the effects of surface topography on implant osseointegration. The development of implant surfaces topography has been empirical, requiring numerous in vitro and in vivo tests. Most of these tests were not standardized, using different surfaces, cell populations, or animal models. The exact role of surface chemistry and topography on the early events of the osseointegration of dental implants remains poorly understood. Objective: The aim of this study was to consider the major claims made concerning the effects of titanium implant surface topography on osseointegration. The osseointegration rate of titanium dental implants is related to their composition and surface roughness. The different methods used for increasing surface roughness or applying osteoconductive coatings to titanium dental implants were reviewed. Important findings of consensus were highlighted, and existing controversies were revealed. Methods: This paper considered many of the research publications listed in Medical Literature Analysis and Retrieval System Online and presented in biomedical research publications and textbooks. Surface treatments, such as titanium plasma spraying, grit blasting, acid etching, alkaline etching, anodization, polymer demixing, sol-gel conversion, and their corresponding surface morphologies and properties were described. Results: Many in vitro evaluations are not predictive of or correlated with in vivo outcomes. In some culture models, increased surface topography positively affects proosteogenic cellular activities. Many studies reveal increase in bone-to-implant contact (BIC), with increased surface topography modifications on implant surfaces. Conclusions: Increased implant surface topography improves the BIC and the mechanical properties of the enhanced interface.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".