Biomechanical and Histomorphometrical Evaluation of <scp>TiZr</scp> Alloy Implants: An in vivo Study in the Rabbit
Bibliographic record
Abstract
BACKGROUND: Clinically, there is a demand for mechanically stronger alloyed implants; however, not much evidence exists with regard to these materials. PURPOSE: To test the osseointegration property of TiZr1317 implants in a rabbit model. MATERIALS AND METHODS: Hydrophilic titanium-zirconium alloy (TiZr1317) implants with sand-blasted and acid-etched surface (test) and hydrophilic cpTi implants with the same treatment (control) were placed pairwise in the hind limbs (two in each tibia and one in each femur) of 36 Swedish lop-eared rabbits. After 2, 4, and 12 weeks (n = 12/time point), the bone samples were subjected to removal torque (RTQ, proximal tibia and femur) and histologic/histomorphometric (distal tibia) testings. RESULTS: The control presented significantly higher RTQ than the test at 2 weeks (55 vs 36 Ncm). No differences were observed for other time points. The test presented higher mean BIC than the control (19.25 vs 13.89 %) at 4 weeks; however, there were no statistical differences for the following time point tested in vivo.The new bone area was significantly higher for the test at 4 weeks in the marrow areas. CONCLUSION: The TiZr1317 implants presented comparable biologic outcomes to that of the cpTi implants through a 12-week evaluation period.
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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) | 0.001 | 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".