The use of bone morphogenetic protein-2 compared with platelet-derived growth factor for the treatment of nonunion
Bibliographic record
Abstract
Aims: The management of nonunions continues to challenge orthopaedic surgeons. The application of growth factors represents a potential strategy to promote healing in such patients. The aim of this study was to compare two commercially available growth factors, bone morphogenetic protein-2 (BMP-2) and platelet-derived growth factor (PDGF), to assess their individual and relative efficacy in a small animal model of nonunion. Methods: A total of 50 male Fischer 344 rats received one of five forms of treatment for a femoral diaphyseal defect: 1) control; 2) PDGF carrier; 3) PDGF treatment; 4) BMP-2 carrier; and 5) BMP-2 treatment. After ten weeks, radiographs were assessed for bone formation and union, with the femora being subjected to micro-CT analysis and biomechanical testing. Results: BMP-2 treatment resulted in a 100% rate of radiological union. This was significantly different from all other groups (p < 0.05), with a correspondingly significantly higher mean radiological score than the PDGF treatment group (p =0.004). Similarly, micro-CT analysis demonstrated a significantly increased mean bone volume and bone volume fraction with BMP-2 treatment compared with PDGF (p < 0.01). Under mechanical testing, the BMP-2 treatment group also demonstrated significantly increased maximum stiffness compared with all other groups (p < 0.05), with significantly increased ultimate torque and yield point compared with the PDGF treatment group (p < 0.001). Conclusion: BMP-2 treatment resulted in significantly improved healing compared with PDGF treatment in a small animal model of nonunion, using clinically relevant carriers. BMP-2 may thus be superior to PDGF in the treatment of nonunions and segmental 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 | 0.000 | 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 | 0.001 |
| 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".