3D‐printed, bioactive ceramic scaffold with rhBMP‐2 in treating critical femoral bone defects in rabbits using the induced membrane technique
Bibliographic record
Abstract
Abstract Although autogenous bone grafts are an optimal filling material for the induced membrane technique, limited availability and complications at the harvest site have created a need for alternative graft materials. We aimed to investigate the effect of an rhBMP‐2‐coated, 3D‐printed, macro/microporous CaO–SiO2–P2O5–B2O3 bioactive ceramic scaffold in the treatment of critical femoral bone defects in rabbits using the induced membrane technique. A 15‐mm segmental bone defect was made in the metadiaphyseal area of the distal femur of 14 rabbits. The defect was filled with polymethylmethacrylate cement and stabilized with a 2.0 mm locking plate. After the membrane matured for 4 weeks, the scaffold was implanted in two randomized groups: Group A (3D‐printed bioceramic scaffold) and Group B (3D‐printed, bioceramic scaffold with rhBMP‐2). Eight weeks after implantation, the radiographic assessment showed that the healing rate of the defect was significantly higher in Group B (7/7, 100%) than in Group A (2/7, 29%). The mean volume of new bone formation around and inside the scaffold doubled in Group B compared to that in Group A. The mean static and dynamic stiffness were significantly higher in Group B. Histological examination revealed newly formed bone in both groups. Extensive cortical bone formation along the scaffold was found in Group B. Successful bone reconstruction in critical‐sized bone defects could be obtained using rhBMP‐2‐coated, 3D‐printed, macro/microporous bioactive ceramic scaffolds. This grafting material demonstrated potential as an alternative graft material in the induced membrane technique for reconstructing critical‐sized bone defects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".