HIGHER URINARY POTASSIUM IS ASSOCIATED WITH DECREASED STONE GROWTH AFTER SHOCK WAVE LITHOTRIPSY
Bibliographic record
Abstract
PURPOSE: We correlated serum and urinary biochemical parameters with radiological evidence of stone growth after shock wave lithotripsy. MATERIALS AND METHODS: Biochemical parameters in serum and 24-hour urine collections of 359 patients were correlated with stone growth for 2 years after shock wave lithotripsy. Each patient underwent a minimum of 2 radiological studies at 3 and 12 months and plain abdominal x-ray at 24 months. The presence and size of stones were documented by a radiologist in blinded fashion. Stone growth was defined as measurable growth of a preexisting stone or new stone formation. RESULTS: A total of 209 patients remained stone-free or had no existing stone growth, while stone size decreased in 30. Of the remaining 120 patients with stone growth 72 had new growth and 48 had growth of preexisting stones. Urinary excretion of potassium was significantly higher in those without than with stone growth (mean 24-hour urine collection plus or minus standard deviation 62 +/- 27 versus 54 +/- 23 mmol., p = 0.009). The only parameter significantly associated with stone growth was urinary potassium. Linear regression revealed that for each 10 unit increase in urinary potassium there was a corresponding 2 mm. decrease in stone growth (p = 0.013). CONCLUSIONS: Our results indicate that increased urinary potassium excretion correlates with a decreased risk of stone growth up to 2 years after shock wave lithotripsy, implying that a high potassium diet may be beneficial for preventing stone growth. The effect of potassium supplementation on stone formation and growth must be investigated further.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".