3D Printed Polyurethane Scaffolds for the Repair of Bone Defects
Bibliographic record
Abstract
Critical-size bone defects are those that will not heal without intervention and can arise secondary to trauma, infection and surgical resection of tumours. Treatment options are currently limited to filling the defect with autologous bone, of which there is not always an abundant supply, or ceramic pastes that only allow for limited osteo-inductive and -conductive capacity. In this study we investigate the repair of bone defects using a 3D printed LayFomm scaffold. LayFomm is a polymer blend of polyvinyl alcohol (PVA) and polyurethane (PU). It can be printed using the most common method of 3D printing, fused deposition modelling, before being washed in water-based solutions to remove the PVA. This leaves a more compliant, micro-porous PU elastomer. In vitro analysis of dental pulp stem cells seeded onto macro-porous scaffolds showed their ability to adhere, proliferate and form mineralised matrix on the scaffold in the presence of osteogenic media. Subcutaneous implantation of LayFomm in a rat model showed the formation of a vascularised fibrous capsule, but without a chronic inflammatory response. Implantation into a mandibular defect showed significantly increased mineralised tissue production when compared to a currently approved bone putty. While their mechanical properties are insufficient for use in load-bearing defects, these findings are promising for the use of polyurethane scaffolds in craniofacial bone regeneration.
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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 | 0.001 | 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".