Importance of Ca<sup>2+</sup> Modifications for Osseointegration of Smooth and Moderately Rough Anodized Titanium Implants – A Removal Torque and Histological Evaluation in Rabbit
Bibliographic record
Abstract
BACKGROUND: Incorporation of Ca(2+) into the titania of anodized titanium surfaces has been found to enhance osseointegration. It provides a stable surface when the ions are incorporated into the oxide layer during the anodizing process. The Ca(2+) may suggestively be prominent sites for mineral induction, attract proteins, and catalyze intracellular cascades. PURPOSE: The aim of the present study was to evaluate the osseointegration of smooth (S(a) < 0.5 µm) and moderately rough (S(a) 1.0-2.0 µm) commercially pure titanium implants, with and without Ca(2+) , in order to reflect on the importance of surface chemistry in relation to topography. MATERIALS AND METHODS: Anodized implants with (OxCa) or without Ca(2+) (Ox), blasted implants (Bl), and blasted anodized implants, with (BlOxCa) or without Ca(2+) (BlOx), were inserted in rabbit femur and tibia. The implant surfaces were characterized using interferometry, scanning electron microscopy, and X-ray photoelectron spectroscopy prior to implant installation. Removal torque (RTQ) measurements were executed on all implants after a healing period of 12 weeks. The implants were, thereafter, removed en bloc with surrounding tissues and prepared for histological evaluations. RESULTS: RTQ measurements of tibial implants revealed significantly higher values for BlOxCa implants (90.7 ± 23.3 Ncm) compared to OxCa (64.6 ± 18.2 Ncm) and BlOx implants (69.7 ± 17.5 Ncm) (p = 0.029). Ca(2+) modification of smooth implants placed in the femur did not reveal any differences. CONCLUSION: Ca(2+) modification of smooth implants resulted in similar interfacial shear strength as moderately rough implants and Ca(2+) modification of moderately rough implants demonstrated the significantly strongest interfacial shear strength when placed in rabbit tibia. This possibly demonstrated surface chemistry compensating for lesser roughness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".