Orthotopic Bone Regeneration within 3D Printed Bioceramic Scaffolds with Region‐Dependent Porosity Gradients in an Equine Model
Bibliographic record
Abstract
The clinical translation of three-dimensionally printed bioceramic scaffolds with tailored architectures holds great promise toward the regeneration of bone to heal critical-size defects. Herein, the long-term in vivo performance of printed hydrogel-ceramic composites made of methacrylated-oligocaprolactone-poloxamer and low-temperature self-setting calcium-phosphates is assessed in a large animal model. Scaffolds printed with different internal architectures, displaying either a designed porosity gradient or a constant pore distribution, are implanted in equine tuber coxae critical size defects. Bone ingrowth is challenged and facilitated only from one direction via encasing the bioceramic in a polycaprolactone shell. After 7 months, total new bone volume and scaffold degradation are significantly greater in structures with constant porosity. Interestingly, gradient scaffolds show lower extent of remodeling and regeneration even in areas having the same porosity as the constant scaffolds. Low regeneration in distal regions from the interface with native bone impairs ossification in proximal regions of the construct, suggesting that anisotropic architectures modulate the cross-talk between distant cells within critical-size defects. The study provides key information on how engineered architectural patterns impact osteoregeneration in vivo, and also indicates the equine tuber coxae as promising orthotopic model for studying materials stimulating bone formation.
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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".