Effect of Potassium Citrate on Calcium Phosphate Stones in a Model of Hypercalciuria
Bibliographic record
Abstract
Potassium citrate is prescribed to decrease stone recurrence in patients with calcium nephrolithiasis. Citrate binds intestinal and urine calcium and increases urine pH. Citrate, metabolized to bicarbonate, should decrease calcium excretion by reducing bone resorption and increasing renal calcium reabsorption. However, citrate binding to intestinal calcium may increase absorption and renal excretion of both phosphate and oxalate. Thus, the effect of potassium citrate on urine calcium oxalate and calcium phosphate supersaturation and stone formation is complex and difficult to predict. To study the effects of potassium citrate on urine supersaturation and stone formation, we utilized 95th-generation inbred genetic hypercalciuric stone-forming rats. Rats were fed a fixed amount of a normal calcium (1.2%) diet supplemented with potassium citrate or potassium chloride (each 4 mmol/d) for 18 weeks. Urine was collected at 6, 12, and 18 weeks. At 18 weeks, stone formation was visualized by radiography. Urine citrate, phosphate, oxalate, and pH levels were higher and urine calcium level was lower in rats fed potassium citrate. Furthermore, calcium oxalate and calcium phosphate supersaturation were higher with potassium citrate; however, uric acid supersaturation was lower. Both groups had similar numbers of exclusively calcium phosphate stones. Thus, potassium citrate effectively raises urine citrate levels and lowers urine calcium levels; however, the increases in urine pH, oxalate, and phosphate levels lead to increased calcium oxalate and calcium phosphate supersaturation. Potassium citrate induces complex changes in urine chemistries and resultant supersaturation, which may not be beneficial in preventing calcium phosphate 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".