Histomorphometric and Mechanical Evaluation of the Bone‐Tissue Response to Implants Prepared with Different Orientation of Surface Topography
Bibliographic record
Abstract
BACKGROUND: Several studies have shown that in soft tissue, the orientation of surface topography affects cell response. However, the response of hard tissue to the orientation is not fully understood. PURPOSE: This study was undertaken to determine how orientation of the microstructure of implant surfaces influences bone healing. MATERIALS AND METHODS: Forty cylindrical implants were prepared with a dominant orientation of the microstructure (20 with a horizontal orientation and 20 with a vertical orientation) and investigated in vivo. Three methods for surface topographic characterization were used to investigate the topography in different resolution levels. RESULTS: Topographic analysis showed that a clear orientation was achieved at the implant surface, and a rougher surface structure was found on the vertically oriented grooves than on the horizontal ones. The implants were inserted in the tibia of 10 New Zealand White rabbits. Pull-out tests and histomorphometric analyses of ground-sections were made after 12 weeks of healing. The pull-out test showed a higher mean value for the vertically grooved implants, but the difference was not significant. Histomorphometric analyses showed no statistically significant differences between the horizontally and the vertically grooved implants when measuring bone-to-implant contact or amount of bone area around the implant. CONCLUSIONS: No advantages could be found for either the horizontal or vertical orientation compared with each other, but further studies are needed in which implant roughness should be similar between the different topographies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".