The Role of Growth Factors on Acceleration of Bone Regeneration During Distraction Osteogenesis
Bibliographic record
Abstract
The distraction osteogenesis (DO) technique has been used worldwide to treat many complex orthopedic and craniofacial conditions. One limitation of this technique is the long time of fixator needs to be left in place until the bone is completely consolidated. Various biophysical, mechanical, and biological methods have been investigated to accelerate bone regeneration during DO. Several growth factors (GFs) are known to enhance bone regeneration such as bone morphogenic proteins, transforming growth factor beta, fibroblast growth factor, insulin growth factor, vascular endothelial growth factor, and platelet-derived growth factor. These GFs are known to stimulate cellular growth, proliferation, migration, and differentiation. In this review, an extensive overview of these GFs development and applications on acceleration of bone regeneration in the context of DO is discussed. Current challenges and alternative tissue engineering techniques to address the delivery and sustain release of these factors are also discussed. Finally, we highlighted our view regarding the remaining questions and future research directions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".