Medical treatment of uric acid kidney stones
Bibliographic record
Abstract
INTRODUCTION: The prevalence of uric acid (UA) stones increases regularly due to its high correlation with obesity, hypertension, metabolic syndrome, type 2 diabetes, and aging. Uric acid stone formation is mainly due to an acidic urinary pH secondary to an impaired urinary ammonium availability responsible for UA rather than soluble urate excretion. Alkalization of urine is therefore advocated to prevent UA crystallization and considered effective therapy. METHODS: We report a large series of 120 patients with UA lithiasis who were successfully treated with potassium (K)-citrate for stone dissolution (n=75) and/or stone recurrence prevention (n=45) without any urologic intervention, with a median 3.14 years followup. The K-citrate was diluted in 1.5 L of water, avoiding gastrointestinal disorders. RESULTS: Among 75 patients having stones in their kidney at initiation of therapy, a complete chemolysis was obtained in 88% of cases. Stone risk factors decreased under treatment, mainly due to increased diuresis, urinary pH, and citrate excretion. Treatment was stopped in only 2% of patients due to side effects, with no hyperkalemia onset despite a median urinary potassium increase of 44 mmol/day. CONCLUSIONS: Contrary to other reports, our data show that medical treatment of UA kidney stones is well-tolerated and efficient if regular monitoring of urinary pH is performed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".