Relationship between gastroesophageal reflux and chronic kidney disease: A meta-analysis of 4 million patients
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) has been associated with higher risk of gastrointestinal disorders, particularly Gastroesophageal reflux disease (GERD). However, the magnitude of this association and the underlying mechanisms remains unclear. METHODS: A systematic search was conducted across major databases from inception to November 2024. We included cross-sectional and case-control studies evaluating the relationship between CKD and GERD. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and prevalence rates. Study quality was assessed using the Newcastle-Ottawa Scale, and heterogeneity was evaluated using the Cochran's Q test and I² statistic. RESULTS: Nine studies involving 4,650,709 participants were included. The pooled prevalence of GERD among CKD patients was 18% (95% CI: 0.10-0.26, I² =93.64%). The pooled crude OR for the association between CKD and GERD was 2.53 (95% CI: 1.30-4.92) and adjusted OR was 1.48 (95% CI: 1.05-2.08). CONCLUSION: This meta-analysis reveals a marginally significant association between CKD and GERD, highlighting higher prevalence of GERD among individuals with CKD. Furthers studies are needed to elucidate the underlying pathophysiological mechanisms and potential clinical implications.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".