Diabetes and Periodontal Diseases: Interplay and Links
Bibliographic record
Abstract
The association between diabetes and periodontal diseases is well-established. Diabetes is a risk factor for periodontal disease, with diabetic patients exhibiting an increased prevalence, extent and severity of gingivitis and perio- dontitis compared to healthy adults. Several mechanisms involved in the pathogenesis of diabetes have also been associated with periodontal disease progression. It is recognized today that there is a bidirectional relationship between diabetes and periodontal disease, with recent research showing that periodontal disease may affect the metabolic control of diabetes in diabetic patients. In this review, we present the current knowledge of the interplay between periodontal diseases and diabetes through the evaluation of randomized control and longitudinal cohort studies published in the past 15 years. Current data support the conclusion that diabetic patients are at increased risk for periodontal diseases, and that patients with poorly controlled diabetes are at risk for severe periodontitis. This results in the destruction of oral connective tissue and generalized bone loss, leading ultimately to tooth loss. Although the effect of periodontal disease on glycemic control in type 1 diabetic patients is controversial, evidence does show a direct correlation between periodontal health and glycemic control in type 2 diabetic patients. Furthermore, several studies have demonstrated the beneficial effect of periodontal treatment on metabolic control of type 2 diabetic patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".