Frequency of Metabolic Abnormalities in Pakistani Children With Renal Stones
Bibliographic record
Abstract
Objective: To determine the frequency of various metabolic abnormalities in children with urinary lithiasis. Methods: This cross-sectional study was conducted at the Armed Forces Institute of Urology, Rawalpindi, from 30 January 2017 to 1 February 2020. A total of 1355 children who were aged 4 to 14 years and who had renal stones were included, while those with urinary tract infections, posterior urethral valve, pelvi-ureteric junction obstruction, reflux disease, and chronic renal failure were excluded. Twenty-four-hour urine samples were analyzed for urinary uric acid, calcium, oxalate, citrate, and magnesium. Demographics and metabolic abnormalities—hypercalciuria, hyperoxaluria, hypocitraturia, hyperuricosuria, and hypomagnesuria—were noted and analyzed. Results: The study analysis included 1355 patients. Low urine volume was observed in 465 (34.3%) of the patients. Three hundred nine patients (22.8%) had metabolic abnormalities, the most common being hypocitraturia (184, 59.5%) followed by hypercalciuria (136, 44%) and hypomagnesuria (126, 40.8%). Mean age of presentation, disease duration, recurrent bilateral stones were found significantly different in those having metabolic abnormalities (7.81±2.25 versus 8.76±2.50 P < 0.001, 7.73±1.50 versus 8.43±1.54 P < 0.001, 19.4 versus 2.4% P < 0.001 respectively). No significant difference was found in frequency of abnormal urinary metabolic parameters between boys and girls (P > 0.05) or, upon data stratification, on the basis of disease duration, stone laterality, and recurrence. Conclusion: Metabolic abnormalities were found in 22.8% % of children presenting with urinary lithiasis. The most frequent abnormality observed was hypocitraturia followed by hypercalciuria and hypomagnesuria. Early identification helps manage such patients appropriately, mitigating long-term sequelae.
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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 | 0.001 |
| 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) | 0.002 | 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".