Mechanical Property Assessment of Bone Healing around a Titanium–Zirconium Alloy Dental Implant
Bibliographic record
Abstract
BACKGROUND: It has been reported that titanium-zirconium alloy with 13-17% zirconium (TiZr1317) implants show higher biomechanical stability and bone area percentage relative to commercially pure titanium (cpTi) grade 4 fixtures. PURPOSE: This study aimed to determine whether the higher stability for TiZr1317 implants is associated with higher mechanical properties of remodeling bone in the areas around the implants. MATERIALS AND METHODS: This study utilized 36 implants (n = 18: TiZr1317, n = 18: cpTi), which were placed in the healed ridges of the mandibular premolar and first molar of 12 mini pigs (n = 3 implants/animal). After 4 weeks in vivo, the samples were retrieved, and resin-embedded histologic sections of approximately 100 μm in thickness were prepared. In order to determine the nanomechanical properties, nanoindentation (n = 30 tests/specimen) was performed on the bone tissue of the sections under wet conditions with maximum load of 300 μN (loading rate: 60 μN/s). RESULTS: The mean (± standard deviation) elastic modulus (E) and hardness (H) for the TiZr1317 group were 2.73 ± 0.50 GPa and 0.116 ± 0.017 GPa, respectively. For the cpTi group, values were 2.68 ± 0.51 GPa and 0.110 ± 0.017 GPa for E and H, respectively. Although slightly higher mechanical properties values were observed for the TiZr1317 implants relative to the cpTi for both elastic modulus and hardness, these differences were not significant (E = p > 0.75; H = p > 0.59). CONCLUSIONS: The titanium-zirconium alloy used in this study presented similar degrees of nanomechanical properties to that of the cpTi implants.
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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 | 0.000 | 0.000 |
| 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".