Does the Urinary Calcium/Citrate Ratio Add to the Diagnostic Workup of Children at Risk of Kidney Stones? A Cross-Sectional Study
Bibliographic record
Abstract
The purpose of the study was to evaluate urinary citrate/creatinine (UCi/UCr) and urinary calcium/citrate (UCa/UCi) ratios for distinguishing stone formers (SF) from non-stone formers (NSF) in an at-risk population. This was a retrospective study that included all pediatric patients who underwent urinary citrate testing from April 2017 to March 2018. The urinary levels of citrate, calcium, sodium, potassium, creatinine, oxalate, urate, pH, and specific gravity (SG) were measured in our clinical laboratory. Diagnosis of kidney stones was obtained through chart review. A total of 97 patients were included (46 NSF and 51 SF). The UCi/UCr ratio was not significantly different between NSF and SF. Median UCa/UCr ratio was higher in SF (0.67) compared with NSF (0.21, p < 0.0001). The median ratio of UCa/UCi was also higher in SF (1.30) than in NSF (0.65, p = 0.001). Oxalate, urate, pH, SG, and urinary sodium/potassium ratio did not differentiate between the SF and NSF. Positive correlation was seen between UCa/UCr and urinary sodium/creatinine UNa/UCr (p < 0.0001), as well as between UCa/UCr and UCi/UCr (p < 0.0001). The study has demonstrated significantly higher UCa/UCi and UCa/UCr in SF compared with NSF, while the use of urinary oxalate, urate, pH, and SG did not differentiate between SF from NSF. We also confirmed a positive correlation between UNa/UCr and UCa/UCr. While the utility of UCa/UCr is well established, our data suggest that UCa/UCi rather than UCi/UCr may be more predictive in the clinical setting when evaluating for nephrolithiasis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".