Periodontitis Impact in Interleukin-6 Serum Levels in Solid Organ Transplanted Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review aimed to investigate the influence of periodontitis on post-transplant IL-6 serum levels of solid organ transplanted patients as compared to healthy subjects. Four databases (PubMed, Scholar, EMBASE, and CENTRAL) were searched up to February 2020 (PROSPERO CRD42018107817). Case-control and cohort studies on the association of IL-6 serum levels with a periodontal status of patients after solid organ transplantation were included. The risk of bias of observational studies was assessed through the Newcastle-Ottawa Scale (NOS). Random effects meta-analyses were thoroughly conducted. GRADE assessment provided quality evidence. Four case-control studies fulfilled the inclusion criteria (274 transplant recipients and 146 healthy controls), all of low risk of bias. Meta-analyses revealed significantly higher IL-6 levels in transplanted patients than healthy individuals with low-quality evidence (Mean Difference (MD): 2.55 (95% confidence interval (CI): 2.07, 3.03)). Transplanted patients with periodontitis have higher serum IL-6 levels than transplanted patients without periodontitis with moderate quality evidence (MD: 2.20 (95% CI: 1.00, 3.39)). We found low-quality evidence of higher IL-6 levels than healthy patients in patients with heart and kidney transplant. In these transplanted patients, there was moderate quality evidence that periodontitis is associated with higher IL-6 serum levels. Future research should consider the impact of such a difference in organ failure and systemic complications.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".