Salivary changes in chronic kidney disease and in patients undergoing hemodialysis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This study is aimed at describing changes in salivary flow rate and ionic composition present in the saliva of chronic kidney disease (CKD) patients by assessing the pH, calcium, phosphate, and phosphorus concentrations and comparing them to healthy individuals, along with exploring the influence of hemodialysis on these parameters. METHODS: The bibliographical search was performed in nine databases to find all types of studies, including observational clinical studies, without restrictions regarding publication year or language. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using JBI tools. Random-effect meta-analysis was performed with the standardized mean difference (SMD) as effect estimate, at a 95% confidence interval. RESULTS: Thirty-three studies were included in the qualitative synthesis and 31 studies were included in the meta-analysis. Chronic kidney disease patients presented lower salivary flow rate (SMD: - 1.73; 95% CI = - 2.14; - 1.31), higher pH (SMD: 1.57; 95% CI = 1.11; 2.03), and higher phosphorus concentration (SMD: 0.86; 95% CI = 0.63; 1.09) in saliva. Concurrently, salivary flow rate and pH presented significant changes after hemodialysis, with higher salivary flow rate (SMD: 0.53; 95% CI = 0.25; 0.81) and lower pH (SMD: - 0.53; 95% CI = - 0.88; - 0.19) in patients on hemodialysis treatment. CONCLUSION: Chronic kidney disease patients present reduced salivary flow rate and increased pH and phosphorus concentration in saliva. Hemodialysis can increase the salivary flow rate of these patients.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".