In Vivo Bone Engineering in a Rabbit Femur
Bibliographic record
Abstract
The repair of bone defects in reconstructive surgery has significant limitations. Donor site morbidity, limited supply of autograft, and risks and complications associated with allografting and synthetic bone substitutes are among the most significant. In an effort to address these problems, the search for an ideal bone replacement has led to the development of a new method of poly(lactide-co-glycolide) (PLGA) foam processing, enabling the production of a biodegradable scaffold with similar porosity to human trabecular bone. In this study, these scaffolds were evaluated for bone repair in vivo in a femoral critical-sized segmental defect in New Zealand White (NZW) rabbits. Three groups of nine animals were investigated. In the first group, the critical-sized defects were empty. Scaffolds alone were implanted in the second group, whereas autologous bone marrow cell-loaded scaffolds were implanted in the third group. Animals ambulated freely for 8 weeks after surgery, and bone formation throughout the defects was serially assessed radiographically and quantified using a bone formation index (BFI) measure. Postmortem radiography and histology were also undertaken to examine bone formation. There was a significant effect of applying this technology to the amount of bone formed in the defects as determined by the BFI (F = 3.41, P < 0.05). The mean BFI for the cell-loaded scaffolds was greater than for the control group at all measured time points (2-, 4-, 6-, and 8-week radiographs). This difference was significant for the 2- and 8-week radiographs (P < 0.05). Qualitative histological assessment confirmed these findings. We concluded from these findings that these PLGA scaffolds loaded with marrow-derived progenitor cells yield significant bone formation in a critical-sized rabbit femoral defect. This technology comprising a novel scaffold design and autologous cells may provide an alternative to current strategies for reconstruction of bony 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".