Effect of Nutritional Interventions on Serum C-reactive Protein Levels in Patients with Periodontitis: A Comprehensive Meta-Analysis
Bibliographic record
Abstract
Elevated level of serum C-reactive protein, a systemic inflammation biomarker, is associated with periodontitis, a common inflammatory condition. This meta-analysis aimed to determine the effects of nutritional interventions on serum C-reactive protein levels in patients with periodontitis. We searched the Cochrane Library, PubMed, Scopus, and Web of Science databases according to PRISMA guidelines, including articles published until December 2023. The articles were selected according to predetermined inclusion criteria, and data extraction and quality assessment were conducted utilizing the Newcastle-Ottawa Scale and standardized forms. Seven out of 438 identified articles met the inclusion criteria. We found that patients with periodontitis had a statistically significant correlation between nutritional interventions and decreased serum C-reactive protein levels (p < 0.05). The interventions included specified dietary modifications and dietary supplements, including vitamin C and folic acid. Diverse patient demographics and intervention categories were observed across the identified articles. Thus, this meta-analysis confirmed that nutritional interventions can reduce serum C-reactive protein levels in patients with periodontitis; therefore, dietary modification is essential in managing the systemic inflammation associated with 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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".