Tea intake and risk of incident kidney stone: A meta-analysis
Bibliographic record
Abstract
Abstract Background: The formation of kidney stones is multifactorial and surveys have shown that not all fluids are equally beneficial in reducing the risk of kidney stones. Multiple studies have shown an association between tea intake and kidney stones. However, studies investigating the relationship between tea intake and the occurrence of kidney stones have been inconsistent. Objective: To clarify this association, we conducted this analysis to determine the link between tea intake and kidney stones. Study design: This study was a meta-analysis. PubMed, Web of Knowledge, Elsevier Science Direct, and Springer digital libraries were searched for studies reporting tea intake and kidney stones. A random-effects model was used to summarize the relationship between tea and kidney stones. The included articles were assessed for quality using the Newcastle–Ottawa scale. Results: A total of ten articles and 14 studies (men and women) were retrieved, including 9 cohort studies, 5 case-control studies, with a total of 1,318,071 participants and of 22,963 kidney stone patient. The results showed that tea intake was negatively correlated with kidney stone, (combined odds ratio [OR], 0.86; 95% confidence interval [CI], 0.81−0.91) with mild heterogeneity (I2=56.6.0%; P=0.005). Subgroup and sensitivity analyses confirmed the results. Conclusions: Tea intake was shown a potential protective effect on the development of kidney stones.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.062 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".