The Performance of Titanium‐Zirconium Implants in the Elderly: A Biomechanical Comparative Study in the Minipig
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to test the following hypothesis: (1) Aging would adversely affect bone integration of implants; (2) Titanium-zirconium-SLActive (TiZr-SLActive) implant might perform better in osseointegration than Ti-SLA implant in aged group. MATERIALS AND METHODS: Thirteen miniature pigs (six of them were young adult minipigs, with other seven being aged ones.) were enrolled in this study. The right mandibular premolars (P1, P2, P3) and the first molar (M1) were extracted in all animals. Three months later, one TiZr-SLActive and one Ti-SLA implants were inserted into the endentulous area in mandible of each animal. The animals were sacrificed 8 weeks after placement of implants. Implants in the mandibles were used for removal torque (RT) tests, while vertebra and femur being retrieved for bone mineral density (BMD) tests. RESULTS: Implant success rate in the young group was significantly higher than that in the aged group. Higher survival rate of implants was also observed in younger group than that in the aged group but without significant difference. Within the young group, mean value for peak RT of Ti-SLA implants was higher than that of TiZr-SLActive implants without significant difference. In the aged group, the TiZr-SLActive implants showed a higher mean value for peak RT than Ti-SLA implants. No significant difference was found in the mean BMD in both vertebra and femur between different age groups. CONCLUSIONS: Within the limits of this study, aging could negatively affect osseointegration of dental implants. TiZr-SLActive implants might to some degree compensate for the compromise in osseointegration brought by aging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".