Short communication: Stem Cells for Periodontal Tissue Regeneration
Bibliographic record
Abstract
Periodontal disease is an inflammatory condition that causes pathological alterations in the periodontium, potentially leading to tooth loss [1]. In the world, 35% of adults in the population suffer from moderate periodontal disease, while up to 15 % were affected by a more severe form at some stage of their life [2, 3].The periodontium has always proved to be one of the structures with inherent regenerative capacities. It gives rise to osteoblasts, periodontal ligament (PDL) fibroblasts, and cementoblasts[4]. However, the periodontium – which includes the periodontal ligament, root cementum, alveolar bone and gingiva has a limited ability to regenerate once damaged [4]. For decades, periodontists have sought to repair the damage from periodontitis and to achieve regeneration through a variety of non-surgical procedures and surgical procedures that include root surface conditioning, bone graft placement, guided tissue regeneration and the application of growth factors [5-7]. However, current procedures allow the periodontal tissue to be repaired rather than regenerated with some approaches showing some limited unpredictable regenerative outcome [8-12]. Recent advances in tissue engineering and stem cell biology have paved the way to develop novel approaches in the regenerative periodontal therapy or to supplement existing treatment modalities for periodontal disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.028 |
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".