Structural and Qualitative Bone Remodeling Around Repetitive Loaded Implants in Rabbits
Bibliographic record
Abstract
BACKGROUND: Bone mechanical function is regulated by bone quality and bone mineral density (BMD) that reflect bone strength. The preferential alignment of biological apatite (BAp) c-axis/collagen fibers and osteocytes is a determinant factor of bone quality. However, the effect of mechanical loading on bone quality around dental implants is unclear. PURPOSE: The aim of this study was to clarify the effects of mechanical loading on osseointegration, bone volume BMD, and bone quality around dental implants. MATERIALS AND METHODS: Twenty anodized Ti-6Al-4V alloy implants (KYOCERA Co., Kyoto, Japan) were placed in the proximal tibial metaphysis of 10 rabbits. Twelve weeks after surgery, mechanical loading was applied along the long axis of the implant (50 N, 3 Hz, 1,800 cycles, 2 days/week) for 8 weeks. Osseointegration, bone volume, BMD, and bone quality were evaluated using light microscopy, microcomputed tomography, polarized light microscopy, and microbeam X-ray diffractometer. RESULTS: Mechanical loading increased osseointegration, bone volume, and BMD. Bone quality around dental implant was altered with increased osteocyte numbers and the preferential alignment direction and degree of BAp c-axis/collagen fibers. CONCLUSIONS: These findings suggest that mechanical loading effectively induces bone anabolic responses around dental implants. Altered bone quality may upregulate bone strength, contributing to long-term implant stability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".