Periodontitis and systemic parameters in chronic kidney disease: Systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective To perform a systematic review with meta‐analysis to assess recent scientific evidence on the association between periodontitis and systemic parameters/conditions in individuals with chronic kidney disease (CKD). Materials and Methods The search for studies was performed in MedLine/PubMed, Scopus, Web of Science, and BIREME databases. Reference lists of selected articles were also searched. Studies with different epidemiological designs evaluating the influence of exposure to periodontitis on serum markers and mortality in individuals with CKD were eligible for inclusion. Three independent reviewers performed the article selection and data extraction. The assessment of methodological quality used the adapted Newcastle Ottawa Scale. Random effects meta‐analysis was performed to calculate association measurements and 95% confidence intervals. Results In total, 3053 records were identified in the database search, with only 25 studies meeting the eligibility criteria and, of these, 10 studies contributed data for meta‐analysis. Using a random‐effects model, periodontitis was associated with hypoalbuminemia (PR unadjusted = 2.47; 95%CI:1.43–4.26), with high levels of C‐reactive protein (PR unadjusted = 1.35; 95%CI%:1.12–1.64), death from cardiovascular disease (RR unadjusted = 2.29; 95%CI:1.67–3.15) and death from all causes (RR unadjusted = 1.73; 95%CI:1.32–2.27). Conclusions The findings of this review validated a positive association between periodontitis and serum markers and mortality data in individuals with CKD.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.041 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".