The Effects of Titanium Implant Surface Topography on Osseointegration: Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it