Periodontitis severity relationship with metabolic syndrome: A systematic review with meta‐analysis
Bibliographic record
Abstract
Abstract The objective of this study was to investigate the association between periodontitis severity and metabolic syndrome (MetS) through systematic review, registered in PROSPERO: CRD42021232120. Selected articles were independently chosen by three reviewers from six databases, including using article reference lists, up until March 2022. Eligible studies were observational, without language limitation, and in subjects aged at least 18 years. The methodological quality of selected studies was assessed using the Newcastle‐Ottawa Scale. Random effects models calculated summary measurements (odds ratio‐OR, 95% confidence interval, 95%CI). The I 2 test evaluated the statistical heterogeneity of the data. Sensitivity, subgroup, and meta‐regression analyses were performed. For the reliability of evidence, the Grading of Recommendations, Assessment, Development, and Evaluations tool was used. A total of 2133 records were identified, and 14 studies were included comprising 24,567 participants. The summary odds ratio showed a positive association between individuals with moderate (OR adjusted = 1.26; 95%CI = 2.10–5.37; I 2 = 45.85%), and severe periodontitis (OR adjusted = 1.50; 95%CI:1.28–1.71; I 2 = 56.46%), and MetS. Subgroup and meta‐regression analyses showed that study effect size was influenced by year of publication, study design, and MetS diagnostic criteria, contributing to inter‐study variability. The findings showed that moderate and severe levels of periodontitis are associated with MetS, suggesting a possible dose–response effect.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| 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".