Bone Formation at Titanium Implants Prepared with Iso- and Anisotropic Surfaces of Similar Roughness: An in Vivo Study
Bibliographic record
Abstract
BACKGROUND: Implant surface topography influences the bone response after implantation. However, the importance of surface orientation is not known. PURPOSE: The aim of this study was to investigate the bone tissue response and the stability of titanium implants prepared with isotropic and anisotropic surfaces of similar roughness. MATERIALS AND METHODS: A total of 18 implants were divided into two groups and were inserted into the femurs of nine rabbits for 12 weeks. Confocal laser scanning microscopy was used for the topographic description to verify that the two different surfaces were modified as intended. The stability of the implants was recorded by resonance frequency (RF) measurements at insertion and at time of removal, after which the implants were evaluated histomorphometrically. RESULTS: RF measurements showed that implant stability increased with time. However, there was no significant difference between the two different surface modifications at insertion and after 12 weeks. The histomorphometric comparison revealed no statistically significant differences in regard to either bone-to-metal contact or bone area inside the threads. CONCLUSION: Titanium implants prepared with isotropic and anisotropic surfaces of similar roughness integrate similarly to bone during the 3 months after implantation.
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.
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) | 0.000 | 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".