The Effect of Dietary Counselling in Reducing Sodium Consumption Among Hypercalciuric Stone Formers and its Impact on Metabolic Risk Factors
Bibliographic record
Abstract
Abstract Introduction Excessive dietary sodium (Na) consumption is a major health care issue in the developed world and linked to many poor health outcomes. Elevated urinary Na may lead to hypercalciuria and an increase in urinary stone risk. Our study aimed to assess the impact of targeted dietary counseling, and its effect on normalizing urinary Na levels in hypercalciuric stone patients. Methods A retrospective analysis of a prospectively collected metabolic stone clinic database was performed. Patients with hypercalciuria and elevated urine Na on 24-hour urine collection (24-HUC) were counselled by the attending nephrologist, urologist or a registered dietician to limit their intake of dietary Na to < 2g/day in addition to receiving general dietary advice. Repeat metabolic testing was performed at least 6 months later. Logistic regression was used to determine correlations between elevated urinary Ca and Na to other urinary abnormalities and to evaluate the effect of normalizing urinary Na on other urinary parameters. Results Metabolic evaluations from 1184 patients were analyzed. The ninety-eight patients with concomitant hypercalciuria and hypernatriuria were predominantly male (67.3%) and had a higher median BMI than the entire cohort. The presence of elevated urinary Na was also associated with hyperuricosuria (p < 0.001) and hyperphosphaturia (p < 0.001). In follow-up, 59.4% corrected their urinary Na, and 43.8% also had their urinary Ca corrected. Patients who corrected their urinary Na were also more likely to have normal urinary values for volume (p = 0.045), oxalate (p = 0.004), and urate (p = 0.008). Conclusions Targeted dietary counseling can be effective in normalizing both elevated urinary Na and Ca levels in stone patients and may obviate the need for pharmacotherapy for the treatment of hypercalciuria in some 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".