Biomimetic Aspects of Oral and Dentofacial Regeneration
Bibliographic record
Abstract
Biomimetic materials for hard and soft tissues have advanced in the fields of tissue engineering and regenerative medicine in dentistry. To examine these recent advances, we searched Medline (OVID) with the key terms "biomimetics", "biomaterials", and "biomimicry" combined with MeSH terms for "dentistry" and limited the date of publication between 2010-2020. Over 500 articles were obtained under clinical trials, randomized clinical trials, metanalysis, and systematic reviews developed in the past 10 years in three major areas of dentistry: restorative, orofacial surgery, and periodontics. Clinical studies and systematic reviews along with hand-searched preclinical studies as potential therapies have been included. They support the proof-of-concept that novel treatments are in the pipeline towards ground-breaking clinical therapies for orofacial bone regeneration, tooth regeneration, repair of the oral mucosa, periodontal tissue engineering, and dental implants. Biomimicry enhances the clinical outcomes and calls for an interdisciplinary approach integrating medicine, bioengineering, biotechnology, and computational sciences to advance the current research to clinics. We conclude that dentistry has come a long way apropos of regenerative medicine; still, there are vast avenues to endeavour, seeking inspiration from other facets in biomedical research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".