Current interpretations on the in vivo response of bone to additively manufactured metallic porous scaffolds: A review
Bibliographic record
Abstract
Recent advances in the field of metallic additive manufacturing have expanded production capabilities for bone implants to include porous lattice structures. While traditional models of de novo bone formation can be applied to fully dense implant materials, their applicability to the interior of porous materials has not been well-characterized. Unlike other reviews that focus on materials and mechanical properties of lattice structures, this review compiles biological performance from in vivo studies in pre-clinical models only. First, we introduce the most common lattice geometry designs employed in vivo and discuss some of their fabrication advantages and limitations. Then lattice geometry is correlated to quantitative (histomorphometric) and qualitative (histological) assessments of osseointegration. We group studies according to two common implant variables: pore size and percent porosity, and explore the extent of osseointegration using common measures, including bone-implant contact (BIC), bone area (BA), bone volume/total volume (BV/TV) and biomechanical stability, for various animal models and implantation times. Based on this, trends related to in vivo bone formation on the interior of lattice structures are presented. Common challenges with lattice structures are highlighted, including nonuniformity of bone growth through the entirety of the lattice structure due to occlusion effects and avascularity. This review paper identifies a lack of systematic in vivo studies on porous AM implants to target optimum geometric design, including pore shape, size, and percent porosity in controlled animal models and critical-sized defects. Further work focusing on surface modification strategies and systematic geometric studies to homogenize in vivo bone growth through the scaffold interior are recommended to increase implant stability in the early stages of osseointegration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".