Population-based dietary risks for kidney stones
Bibliographic record
Abstract
INTRODUCTION: In the context of the increasing incidence of kidney stones, we aimed to assess the percentage of the population who are eating an at-risk diet for kidney stones and to understand the baseline diet for future counseling. METHODS: The 2015 Canadian Community Health Survey, a national, cross-sectional instrument administered by Statistics Canada and Health Canada, was queried. Intake of relevant nutrients was compared to dietary risk factors for kidney stone formation. Factors associated with nutrient intake were analyzed in a multivariable regression. RESULTS: Data for 14 275 participants was included, of whom 24% consumed >2.5 L of fluid per day and 9.4% consumed 1000-1200 mg of dietary calcium; 53.9% consumed too much sodium but 61% of the population had the recommended protein intake. Ninety-nine percent (99%) of the population had at least one dietary risk factor for kidney stone formation, while 92% had two or more risk factors. Fluid, sodium, calcium, and protein intake increased significantly with education level, income, and if employed (p<0.05 for all); however, fluid, protein, and sodium intake were lower in patients with hypertension and heart disease (p<0.05 for all). CONCLUSIONS: While only a subset of the population will develop stones, this study shows that 99% of the population has a diet that elevates the risk of stone disease. As the incidence of kidney stones increases, population-based dietary interventions should be considered. Furthermore, clinicians may use these data to understand the average diet as a starting point for questioning and counseling patients.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".