[Quantitative mineralogical analyzes of kidney stones and diagnosing metabolic disorders in female patients with calcium oxalate urolithiasis].
Bibliographic record
Abstract
OBJECTIVE: To conduct a complex examination of female patients with calcium oxalate urolithiasis to detect metabolic disorders, leading to stone formation. MATERIALS AND METHODS: The study was carried out using complex physical and chemical methods, including quantitative X-ray phase analysis of urinary stones, pH measurement, volumetry, urine and blood spectrophotometry. RESULTS: Quantitative mineralogical composition of stones, daily urine pH profile, daily urinary excretion of ions of calcium, magnesium, oxalate, phosphate, citrate and uric acid were determined in 20 female patients with calcium oxalate stones. DISCUSSION: We have shown that most of the stones comprised calcium oxalate monohydrate or mixtures of calcium oxalate dihydrate and hydroxyapatite. Among the identified abnormalities, the most frequent were hypocitraturia and hypercalciuria - 90 and 45%, respectively. Our findings revealed that the daily secretion of citrate and oxalate in patients older than 50 years was significantly lower than in younger patients. SUMMARY AND CONCLUSION: In conclusion, daily urinary citrate excretion should be measured in female patients with calcium oxalate stones. This is necessary both to determine the causes of stone formation, and to monitor the effectiveness of citrate therapy.
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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 | 0.001 | 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".