Is it safe to prescribe ascorbic acid for urinary acidification in stone-forming patients with alkaline urine?
Bibliographic record
Abstract
Abstract Objective: To study the effect of ascorbic acid (AA) supplementation on urinary pH, metabolic stone work-up parameters, and development of de novo urolithiasis in stone-forming patients. Material and methods: A retrospective review of the patients followed-up at a tertiary stone centre between September 2009 and October 2015 was performed. Patients with recurrent urolithiasis who received AA supplementation as a urinary acidifying agent were included in the study. Detailed metabolic stone work-up, including two 24-hour urine collections obtained pre- and post-AA supplementation were compared. In addition, imaging studies were reviewed to assess the development of de novo urolithiasis. Results: Twenty-four patients were included in the study with a mean age of 60.6 years and a median daily AA dose of 1000 mg (range: 500-2000 mg). Median follow-up period was 22.6 months (range: 19.7-32.1). After AA supplementation, there was a significant decrease in urinary pH (7.6 vs. 6.9, p=0.02). Although there was no significant increase in the daily oxalate excretion, two patients (8.3%) had their AA dose reduced or discontinued due to de novo hyperoxaluria (342.9 vs 510.2 umol/day; p=0.75). Other serum and urinary parameters did not show any significant changes. Eight (33.3%) patients developed de novo urolithiasis with struvite and carbonate apatite being the major components. Conclusion: AA supplementation resulted in significantly lower urinary pH in patients with recurrent urolithiasis and alkaline urine pH. Prospective studies are needed to assess whether this reduction in urinary pH is associated with lower stone recurrence rates.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".