Biomaterial scaffolds in maxillofacial bone tissue engineering: A review of recent advances
Bibliographic record
Abstract
Maxillofacial bone defects caused by congenital malformations, trauma, tumors, and inflammation can severely affect functions and aesthetics of maxillofacial region. Despite certain successful clinical applications of biomaterial scaffolds, ideal bone regeneration remains a challenge in maxillofacial region due to its irregular shape, complex structure, and unique biological functions. Scaffolds that address multiple needs of maxillofacial bone regeneration are under development to optimize bone regeneration capacity, costs, operational convenience. etc. In this review, we first highlight the special considerations of bone regeneration in maxillofacial region and provide an overview of the biomaterial scaffolds for maxillofacial bone regeneration under clinical examination and their efficacy, which provide basis and directions for future scaffold design. Latest advances of these scaffolds are then discussed, as well as future perspectives and challenges. Deepening our understanding of these scaffolds will help foster better innovations to improve the outcome of maxillofacial bone tissue engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".