Association Between Gallstone Disease and Kidney Stone Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and aims: Gallstone disease (GSD) and kidney stone disease (KSD) have increased due to lifestyle in recent decades. This systematic review and meta-analysis aimed to investigate the association between these two diseases. Methods: A comprehensive electronic database search was conducted before August 25, 2024. This systematic review and meta-analysis included observational studies. The meta-analysis employed a random-effects model to compute the overall summary estimates of the association between GSD and KSD using risk ratios with 95% confidence intervals (CIs) as the primary measure of the effect size. Heterogeneity was evaluated using chi-square tests, the I² statistic, and forest plots. Publication bias was assessed through Begg’s and Egger’s tests. A P value of less than 0.05 was considered statistically significant, and all analyses were performed using Stata 17 software. Results: The meta-analysis included 9 studies encompassing 982847 participants. The pooled analysis revealed a statistically significant association between GSD and KSD, with a risk of 1.78 (95% CI: 1.572.03, P≤0.001). Begg’s and Egger’s tests demonstrated no significant bias (Begg’s test P=0.835, Egger’s test P=0.812). Variables such as study year, sample size, mean age of participants, mean follow-up, and study quality as determined by the Newcastle-Ottawa Scale (NOS) were examined, but none could significantly impact heterogeneity (P>0.10). Conclusion: This systematic review and meta-analysis provide evidence of a significant association between GSD and KSD. Therefore, further investigation into the underlying mechanisms and potential risk factors is necessary.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| 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".