Correlation between rheumatoid arthritis and chronic periodontitis: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of this article is to summarize, compare, and assess possible association in individuals with or without rheumatoid arthritis (RA) for periodontitis. EVIDENCE ACQUISITION: Three study repositories were searched for quantitative studies examining the relationship between periodontal disease and rheumatoid arthritis between 2000 and December 2022. Quality was evaluated using the Newcastle Ottawa Scale (NOS). The standardized mean difference (SMD), with a random effect model and a P value of 0.05 as the significance level, was utilized as a summary statistic measure. EVIDENCE SYNTHESIS: Fourteen papers were included in the descriptive synthesis. Thirteen were qualified for meta-analysis. Our findings suggest a link between the two conditions in terms of clinical attachment levels (CAL), tooth loss, Plaque Index, and probing depth. The estimated SMD for CAL was found to be 0.68 (95% CI: 0.15-1.21) (P<0.01). For tooth loss, the forest plot analysis revealed an SMD of 1.62 (95% CI: 0.48-2.76) (P=0.005). Similarly, for pocket depth, the SMD was 0.53; CI: 0.07-0.99 (P>0.05). The pooled estimates for plaque index were 0.29; CI: 0.03-0.61 (P>0.05). The funnel plot showed a symmetric distribution with the absence of systematic heterogeneity. CONCLUSIONS: Although our data suggest a link between periodontal disease and rheumatoid arthritis, larger population-based investigations are needed to validate this connection. Case-control studies must pave the way to more rigorous investigations with well-defined populations and clinical outcomes as primary outcome measures.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".