Periodontal disease and visfatin level: A systematic review and meta-analysis
Bibliographic record
Abstract
Visfatin is considered an inflammatory biomarker in periodontal disease (PD). In this meta-analysis, we aimed to evaluate the relationship between Visfatin biomarker level with PD. In this study, Medline, Scopus, Web of Science, and Google Scholar were searched. We included studies that examined visfatin levels in samples from healthy people and periodontal disease until March 2023. The quality of the selected articles was evaluated using the Newcastle-Ottawa assessment scale. Depending on heterogeneity of studies, random-effects or fixed-effect models were used to pool results and report the standardized mean difference (SMD). After screening the retrieved papers, the related data were extracted. A total of 159 studies were identified, and 16 studies were included in the meta-analysis. In 9 studies, the SMD of visfatin level of gingival crevicular fluid (GCF) in patients with chronic periodontitis (CP) and healthy individuals was 4.32 (p<0.001). In 6 studies, the SMD of salivary visfatin level in patients with CP and healthy individuals was 2.95 (p = 0.004). In addition, in five studies, the SMD of serum visfatin level in patients with CP and healthy individuals was 7.87 (p<0.001). Therefore, Visfatin levels in serum, saliva, and GCF of patients with CP were increased in comparison to healthy individuals. Comparison of visfatin levels in saliva of gingivitis patients and healthy individuals showed a significant increase of visfatin in gingivitis patients (SMD:0.57, P = 0.018), but no significant difference was observed in the mean GCF visfatin level of gingivitis patients and healthy individuals (SMD:2.60, P = 0.090). In addition, the results suggested that there is no difference between gingivitis cases compared to CP patients (SMD:3.59, P = 0.217). Visfatin levels in GCF, serum, and saliva have the potential to be used as a diagnostic biomarker of periodontitis.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".