Prevalence, Incidence, and Determinants of Kidney Stones in a Nationally Representative Sample of US Adults
Bibliographic record
Abstract
Purpose: To determine the prevalence, incidence, and determinants of kidney stones among adults in the United States. Materials and Methods: This cross-sectional observational study evaluated the lifetime prevalence of kidney stones and the 12-month incidence of kidney stone passage from the 2017 to 2020 National Health and Nutrition Examination Survey. Survey statistical methods were used to estimate kidney stone prevalence and incidence and the association of patient characteristics with these outcomes using logistic regression. Results: The analysis included 9208 participants with prevalence data and 9193 with incidence data. The mean age of the sample was 51 ± 17 years, 49% were male, and the mean body mass index was 30 ± 8 kg/m 2 . The prevalence of kidney stones was 9.9% (95% confidence interval (CI): 8.7%-11.3%), and the incidence of stone passage was 1.8% (95% CI: 1.4%-2.4%). The most important covariates predicting kidney stone prevalence were a history of gallstones (OR = 2.89: 95% CI: 2.16-3.89, P < .001), hypertension (OR = 1.73: 95% CI: 1.06-2.83, P = .03), and chronic kidney disease (OR = 1.99: 95% CI: 1.01-3.90, P = .046). The same variables were most important in predicting the incidence of kidney stone passage: history of gallstones (OR = 2.66: 95% CI: 1.47-4.81, P = .002), chronic kidney disease (OR = 3.34: 95% CI: 1.01-11.01, P = .048), and hypertension (OR = 2.24: 95% CI: 1.17-4.27, P = .02). Conclusions: The self-reported prevalence and incidence of kidney stones in the US adult population between 2017 and 2020 were 9.9% and 1.8%, respectively. History of gallstones, hypertension, and chronic kidney disease were important predictors of both outcomes. Individuals with these risk factors may require more frequent monitoring or targeted preventative lifestyle interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".