Trend and Cancer-Specific Prevalence of Kidney Stones Among US Cancer Survivors, 2007–2020
Bibliographic record
Abstract
Purpose: To evaluate the prevalence and cancer-specific patterns of kidney stones among U.S. cancer survivors compared to non-cancer adults. Methods: This was a serial cross-sectional, descriptive epidemiologic analysis of a US nationally representative sample from the National Health and Nutrition Examination Survey from 2007 to 2020. Weighted prevalence of kidney stones was estimated for both non-cancer adults and cancer survivors by study cycle. Multivariable logistic regression was conducted to examine factors associated with higher probability of kidney stones in both non-cancer adults and cancer survivors. Results: From 2007–2008 to 2017–2020, kidney stone prevalence rose in both non-cancer adults (8.5% to 9.2%, p for trend = 0.013) and cancer survivors (13.1% to 17.3%, p for trend = 0.033). Throughout the study period, prevalence was consistently higher in cancer survivors. The overall prevalence from 2007 to 2020 was 15.8% (95% CI: 14.0–17.5%) in cancer survivors and 9.2% (95% CI: 8.8–9.6%) in non-cancer adults. After adjusting for sociodemographic, lifestyle, and health factors, cancer survivors had higher odds of kidney stones (OR = 1.28, 95% CI: 1.10–1.49). Compared with non-cancer adults, survivors of ovarian (OR = 3.71, 95% CI: 1.77–7.78), kidney (OR = 2.88, 95% CI: 1.46–5.68), bone and soft tissue (OR = 2.86, 95% CI: 1.12–7.30), uterine (OR = 1.94, 95% CI: 1.17–3.22), cervix (OR = 1.68, 95% CI: 1.08–2.61) and prostate (OR = 1.41, 95% CI: 1.06–1.87) cancers were statistically more likely to report kidney stones. The prevalence was numerically highest among survivors of kidney cancer (34.7%), followed by bone and soft tissue (29.9%), ovarian (29.8%), and testicular (26.3%) cancers. Conclusions: The higher prevalence of kidney stones in cancer survivors, with substantial variation by cancer type, highlights the urgent need for effective clinical management of kidney stones in oncology settings and mechanistic research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".