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Record W4414017030 · doi:10.3390/curroncol32090498

Trend and Cancer-Specific Prevalence of Kidney Stones Among US Cancer Survivors, 2007–2020

2025· article· en· W4414017030 on OpenAlexaffvenue
Chao Cao, Ruixuan Wang, Xiangren Wang, Mohammad Abufaraj, Thomas Waldhoer, Geoffrey Gotto, Shahrokh F. Shariat, Lin Yang

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsAlberta Cancer FoundationUniversity of Calgary
FundersNational Cancer Institute
KeywordsMedicineKidney cancerCancerDemographyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.392
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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