Association between kidney stones and future risk of kidney cancer
Bibliographic record
Abstract
INTRODUCTION: Despite increasing interest in the potential associations between kidney stones and kidney cancer, their relationship remains incompletely characterized. This systematic review and meta-analysis evaluated the association between a history of kidney stones and the future risk of kidney cancer. METHODS: We systematically searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for observational studies of renal cell carcinoma risk in adults with kidney stones. A random-effects meta-analysis was performed to calculate the pooled risk ratio and 95% confidence interval (CI). Subgroup analyses and meta-regression were conducted to assess the associations with study design, data sources, risk of bias, control group type, and sex. RESULTS: =94%). Subgroup analysis showed a consistently elevated risk of kidney cancer in stone formers across all subgroups. No significant differences were observed between subgroups, except that more recent studies demonstrated significantly stronger associations between stone disease and risk of kidney cancer (p<0.001). CONCLUSIONS: This meta-analysis demonstrates a significant association between kidney stones and increased risk of kidney cancer, with affected individuals having approximately twice the risk of developing kidney cancer. These findings highlight the importance of enhanced cancer surveillance in patients with a history of kidney stones and suggest the need for further research into shared pathophysiologic mechanisms and potential preventative strategies.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".