Osseointegration of functionally-graded Ti6Al4V porous implants: Histology of the pore network
Bibliographic record
Abstract
Abstract The additive manufacturing of titanium into porous geometries offers a means to generate low-stiffness endosseous implants with a greater surface area to improve osseointegration. In order to optimize pore size in the scaffolds, it is important to first understand the timeline of osseointegration in pre-clinical models. In this work, selective laser melting was used to produce gyroid-based scaffolds with a uniform pore size of 300 μm or functionally-graded pore size from 600 μm to 300 μm before implantation in New Zealand white rabbit tibiae for 4 and 12 weeks. Initial in vitro assessment with Saos-2 cells showed favourable cell proliferation at pore sizes of 300 and 600 μm. At four weeks, histological observations indicated some residual inflammation alongside neovessel infiltration into the scaffold interior and some early apposition of mineralized bone tissue. At twelve weeks, both scaffolds were filled with a mixture of adipocyte-rich marrow, micro-capillaries, and mineralized bone tissue. X-ray microcomputed tomography showed a higher bone volume fraction (BV/TV) and percentage of bone-implant contact (BIC) in the implants with 300 μm pores than in the functionally-graded specimens, indicating that these smaller pore sizes may be favourable for osseointegration in leporine bone. Graphical Abstract
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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) | 0.002 | 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".