Association of serum bicarbonate with the development of kidney stones in patients with chronic kidney disease: a retrospective cohort study
Bibliographic record
Abstract
Background: Epidemiological studies demonstrate an association between kidney stones and risk of chronic kidney disease (CKD) and CKD progression. Metabolic acidosis, as a consequence of CKD, results in a reduced urine pH which promotes the formation of some types of kidney stones and inhibits the formation of others. While metabolic acidosis is a risk factor for CKD progression, the association of serum bicarbonate with risk of incident kidney stones is not well understood. Methods: We used an Integrated Claims-Clinical dataset of US patients to generate a cohort of patients with non-dialysis-dependent CKD with two serum bicarbonate values of 12 to <22 mmol/L (metabolic acidosis) or 22 to <30 mmol/L (normal serum bicarbonate). Primary exposure variables were baseline serum bicarbonate and change in serum bicarbonate over time. Cox proportional hazards models evaluated time to first occurrence of kidney stones during a median 3.2-year follow-up. Results: < .0001). Both lower baseline serum bicarbonate [hazard ratio (HR) 1.047; 95% confidence interval (CI) 1.036-1.057] and decreasing serum bicarbonate over time (HR 1.034; 95% CI 1.026-1.043) were associated with increased risk of kidney stone development. Conclusions: Metabolic acidosis was associated with a higher incidence of kidney stones and shorter time to incident stone formation in patients with CKD. Future studies may investigate the role of correcting metabolic acidosis to prevent stone formation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".