Reply to Editorial Comment: Prevalence, Incidence, and Determinants of Kidney Stones in a Nationally Representative Sample of US Adults
Bibliographic record
Abstract
The authors appreciate the opportunity to respond to this thoughtful editorial1 regarding our recent publication examining the epidemiology of kidney stones in the United States.2 The authors agree that accurately determining the global burden of this disease remains an ongoing challenge due to geographic and methodological differences among studies. The 1.8% annual incidence of kidney stone passage from 2017 to 2020 that the authors reported is consistent with a prior analysis of National Health and Nutrition Examination Survey NHANES data from 2015 to 2018, reporting a 2.1% annual incidence.1 A notable strength of using NHANES data is the ability to derive nationally representative epidemiologic estimates. However, this data source is limited to the US population only and relies on self-reported kidney stone histories. As rightly highlighted in this editorial, reported kidney stone incidence varies considerably among global populations.2 The observed association between self-reported history of kidney stones and gallstone disease is intriguing and warrants additional investigation. Although a definitive causal relationship has not been established, kidney stones and gallstones share common risk factors, including insulin resistance, obesity, and systemic inflammation.3 Elucidating the complex interplay between these risk factors is important for ongoing research and may provide insights into pathogenic mechanisms linking these conditions. Concerted efforts are still needed to better characterize the worldwide burden of kidney stone disease and related comorbidities across diverse populations. Such epidemiological data may facilitate improved risk assessment and implementation of preventative interventions tailored to groups most likely to benefit. Most importantly, it reminds us that kidney stone disease should not be viewed as a completely separate entity and is associated with other risk factors such as metabolic syndrome. Taking a more holistic approach that addresses underlying causes will likely benefit patients more than treating them solely as kidney stone formers. FUNDING Boston Scientific supported this research. CONFLICT OF INTEREST Ben H. Chew reports consultancy with Boston Scientific. Larry E. Miller reports consultancy with Boston Scientific. Brian Eisner reports consultancy with Boston Scientific. Samir Bhattacharyya reports employment with Boston Scientific. Naeem Bhojani reports consultancy with Boston Scientific.
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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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.027 | 0.035 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".