Exploring the Use of Animal Models in Craniofacial Regenerative Medicine: A Narrative Review
Bibliographic record
Abstract
The craniofacial region contains skin, bones, cartilage, the temporomandibular joint (TMJ), teeth, periodontal tissues, mucosa, salivary glands, muscles, nerves, and blood vessels. Applying tissue engineering therapeutically helps replace lost tissues after trauma or cancer. Despite recent advances, it remains essential to standardize and validate the most appropriate animal models to effectively translate preclinical data to clinical situations. Therefore, this review focused on applying various animal models in craniofacial tissue engineering and regeneration. This research was based on PubMed, Scopus, and Google Scholar data available until January 2023. This study included only English-language publications describing animal models' application in craniofacial tissue engineering ( in vivo and review studies). Study selection was based on evaluating titles, abstracts, and full texts. The total number of initial studies was 6454. Following the screening process, 295 articles remained on the final list. Numerous in vivo studies have shown that small and large animal models can benefit clinical conditions by assessing the efficacy and safety of new therapeutic interventions, devices, and biomaterials in animals with similar diseases/defects to humans. Different species' anatomical, physiologic, and biological features must be considered in developing innovative, reproducible, and discriminative experimental models to select an appropriate animal model for a specific tissue defect. As a result, understanding the parallels between human and veterinary medicine can benefit both fields.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".