Bone Formation and Maintenance in Oral Surgery: The Decisive Role of the Immune System—A Narrative Review of Mechanisms and Solutions
Bibliographic record
Abstract
Based on the evidence of a significant communication and connection pathway between the bone and immune systems, a new science has emerged: osteoimmunology. Indeed, the immune system has a considerable impact on bone health and diseases, as well as on bone formation during grafts and its stability over time. Chronic inflammation induces the excessive production of oxidants. An imbalance between the levels of oxidants and antioxidants is called oxidative stress. This physio-pathological state causes both molecular and cellular damage, which leads to DNA alterations, genetic mutations and cell apoptosis, and thus, impaired immunity followed by delayed or compromised wound healing. Oxidative stress levels experienced by the body affect bone regeneration and maintenance around teeth and dental implants. As the immune system and bone remodeling are interconnected, bone loss is a consequence of immune dysregulation. Therefore, oral tissue deficiencies such as periodontitis and peri-implantitis should be regarded as immune diseases. Bone management strategies should include both biological and surgical solutions. These protocols tend to improve immunity through antioxidant production to enhance bone formation and prevent bone loss. This narrative review aims to highlight the relationship between inflammation, oxidation, immunity and bone health in the oral cavity. It intends to help clinicians to detect high-risk situations in oral surgery and to propose biological and clinical solutions that will enhance patients' immune responses and surgical treatment outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".