Studies to identify the basis for an alkaline urine pH in patients with calcium hydrogen phosphate kidney stones
Bibliographic record
Abstract
BACKGROUND: Patients with CaHPO(4) kidney stones belong to a diagnostic category that has a high urine pH as its common feature. Our objective was to provide a new clinical approach to examine the basis for this high pH. METHODS: The study group consisted of 26 CaHPO(4) stone formers and 28 normal volunteers. Urine was collected q2h plus an overnight sample to identify patients with a urine pH > 6.5 for 12/24 h. Urine ammonium (U(NH4)), sulphate (U(SO4)) and citrate were measured and diet net alkali was calculated. RESULTS: Of the 26 patients, 13 had persistently alkaline urine. In 7/13, U(NH4) (68 +/- 13 mEq/day) and U(SO4) (57 +/- 7 mEq/day) were both high. In 6/13 patients, U(NH4) was the usual 31 +/- 3 mEq/day; in 4/6, U(NH4)/U(SO4) was 0.9 +/- 0.1; the cause of the alkaline urine pH seemed to be a dietary alkali load because the rise in urine pH was episodic and coincided with a high net diet alkali load and peak citrate excretion rates. The remaining two patients had a high U(NH4)/U(SO4) (2.2 and 1.6). Citrate excretion was very low in the male, but not in the female patient. CONCLUSIONS: There are heterogeneous causes for a persistently high urine pH. Two of the patients had a possible molecular basis: the lesion could be a low proximal convoluted tubule cell pH in the male and an increased entry of NH(3) into the late distal nephron in the female.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".