Discrete calcium phosphate nanocrystalline deposition enhances osteoconduction on titanium‐based implant surfaces
Bibliographic record
Abstract
We sought to assess the ability of nanotopographically complex titanium surfaces to accelerate osteoconduction. For this, 130 miniature bone ingrowth chambers (called "T plants"), fabricated from either commercially pure titanium (cpTi) or titanium alloy (Ti6Al4V or Ti64), with microtopographically complex surfaces were used in the study, of which 50 were further modified by the discrete crystalline deposition (DCD) of calcium phosphate (CAP) nanoparticles that superimposed a nanotopographic complexity on each implant surface. Thus, four experimental groups were generated (cpTi, cpTi-DCD, Ti64, and Ti64-DCD), and the Tplants were implanted bilaterally in the femora of Wistar rats for 9 days. After harvesting, the femora were trimmed, and multiple-mounted samples were embedded in PMMA. The blocks produced were ground and block faces observed by back-scattering electron imaging (BSEI) at different planes through the chambers. Osteoconduction was assessed, as a function of bone-implant contact, on a total of 1087 BSEI micrographs and submitted to rigorous statistical analyses. Our results showed both the important effects of anatomic location on bone ingrowth and the significant increase in osteoconduction (p < 0.001) as a function of the enhanced surface nanotopography obtained by the CAP nanocrystals.
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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".