Current Concepts in the Management of Periodontitis
Bibliographic record
Abstract
Periodontitis is a common disorder affecting >40% of adults in the United States. Globally, the severe form of the disease has a prevalence of 11%. In advanced cases, periodontitis leads to tooth loss and reduced quality of life. The aetiology of periodontitis is multifactorial. Subgingival dental biofilm elicits a host inflammatory and immune response, ultimately leading to irreversible destruction of the periodontium (i.e. alveolar bone and periodontal ligament) in a susceptible host. In order to successfully manage periodontitis, dental professionals must understand the pathogenesis, primary aetiology, risk factors, contributing factors and treatment protocols. Careful diagnosis, elimination of the causes and reduction of modifiable risk factors are paramount for successful prevention and treatment of periodontitis. Initial non-surgical periodontal therapy primarily consists of home care review and scaling and root planing. For residual sites with active periodontitis at periodontal re-evaluation, a contemporary regenerative or traditional resective surgical therapy can be utilised. Thereafter, periodontal maintenance therapy at a regular interval and long-term follow-ups are also crucial to the success of the treatment and long-term retention of teeth. The aim of this review is to provide current concepts of diagnosis, prevention and treatment of periodontitis. Both clinical and biological rationales will be discussed.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".