Association between rheumatoid arthritis and periodontal disease
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is related to periodontal disease (PD) because both diseases share similar inflammatory pathogenic mechanisms that produce bone resorption. To assess the possible bidirectional link between RA and PD. A search for articles on RA and PD was conducted in the following electronic databases: PubMed (MEDLINE, Cochrane Library), Web of Science (WoS), and Google Scholar. Twenty-two studies with a low-moderate risk of bias according to the Newcastle-Ottawa Methodological Quality Scale were considered in this meta-analysis. The data were analyzed using the Statistical Software RevMan 5.4 (The Cochrane Collaboration, Oxford, UK). For continuous outcomes, the estimates of effects of the intervention were expressed as mean differences (MDs) using the inverse variance method, and for dichotomous outcomes, the estimates of effects of the intervention were expressed as odds ratios (OR) using the Mantel-Haenszel method, both with 95% confidence intervals. Patients with RA showed higher levels of: Plaque index (MD: 0.10; P < 0.001), gingival index (MD: 0.31; P < 0.001), probing depth (MD: 0.45; P < 0.001), clinical attachment loss (MD: 0.59; P < 0.001), and bleeding on probing (MD: 8.06; P < 0.001). They also had a lower number of remaining teeth (MD:-0.80; P = 0.27) and a greater number of missing teeth (MD: 2.70; P < 0.001). These same patients had a higher risk of both moderate (OR: 2.90; P = 0.008) and severe periodontitis (OR: 2.78; P = 0.01). Patients with RA have a higher risk of moderate-severe PD and a worsening of all periodontal parameters.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".