Natural graft tissues and synthetic biomaterials for periodontal and alveolar bone reconstructive applications: a review
Bibliographic record
Abstract
Periodontal disease is categorized by the destruction of periodontal tissues. Over the years, there have been several clinical techniques and material options that been investigated for periodontal defect repair/regeneration. The development of improved biomaterials for periodontal tissue engineering has significantly improved the available treatment options and their clinical results. Bone replacement graft materials, barrier membranes, various growth factors and combination of these have been used. The available bone tissue replacement materials commonly used include autografts, allografts, xenografts and alloplasts. These graft materials mostly function as osteogenic, osteoinductive and/or osteoconductive scaffolds. Polymers (natural and synthetic) are more widely used as a barrier material in guided tissue regeneration (GTR) and guided bone regeneration (GBR) applications. They work on the principle of epithelial cell exclusion to allow periodontal ligament and alveolar bone cells to repopulate the defect before the normally faster epithelial cells. However, in an attempt to overcome complications related to the epithelial down-growth and/or collapse of the non-rigid barrier membrane and to maintain space, clinicians commonly use a combination of membranes with hard tissue grafts. This article aims to review various available natural tissues and biomaterial based bone replacement graft and membrane options used in periodontal regeneration applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.005 | 0.002 |
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".