Periodontitis and chronic kidney disease: a systematic review of the association of diseases and the effect of periodontal treatment on estimated glomerular filtration rate
Bibliographic record
Abstract
AIM: The aim of this systematic review (SR) was to evaluate the association between periodontitis and chronic kidney disease (CKD) and the effect of periodontal treatment (PT) on the estimated glomerular filtration rate (eGFR). METHODS: MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched up to and including September 30, 2012 to observational (S1) and interventional (S2) studies on the association of periodontitis with CKD. Studies were considered eligible for inclusion if they reported the eGFR. Search was conducted by two independent reviewers. The methodological quality of the observational studies was assessed using the Newcastle-Ottawa Scale (NOS) adapted for this review, and the Cochrane's Collaboration risk of bias assessment tool. A random-effects odds-ratio meta-analysis was conducted to estimate the degree of association between periodontitis and CKD. RESULTS: Search strategy identified 2456 potentially eligible articles, of which four cross-sectional, one retrospective, and three interventional studies were included. Four S1, 80.0% reported some degree of association between periodontitis and CKD. Similarly, such an outcome was supported by pooled estimates (OR: 1.65, 95% Confidence Interval: 1.35, 2.01, p < 0.00001, χ(2) = 1.70, I(2 ) = 0%). All interventional studies found positive outcomes related to treatment. CONCLUSION: There is quite consistent evidence to support the positive association between periodontitis and CKD, as well as the positive effect of PT on eGFR.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".