Association between the metabolic profile of urolithiasis in children with idiopathic hypercalciuria and the composition of the stone assessed by infrared spectroscopy
Bibliographic record
Abstract
Introduction Urolithiasis is an increasingly common condition. Each patient after stone passage should have stone analysis performed. Every child with a urinary stone should be given a complete metabolic evaluation, and the stone analysis is an essential component of it. The aim of the study was to establish the relation between the metabolic profile of urolithiasis in children with idiopathic hypercalciuria and the composition of the excreted stone. Material and methods The study included 26 children with urolithiasis (aged 1–17 years) from whom stones were obtained for the analysis. The urine pH and the 24-hour urine excretion of calcium, phosphorus, magnesium and oxalate as well as spot urinalysis including ratio of crystalloids to creatinine from the second voided urine sample of the day were assessed. Urinary stones were analyzed by infrared spectroscopy. The relation between the metabolic data and the stone type was then analyzed, taking into account two types of minerals: stones with a predominance of calcium oxalate dihydrate (weddellite) and calcium oxalate monohydrate (whewellite). Results No correlation was found between the individual serum metabolic parameters of the patient and the composition of excreted stones. Substantially lower urinary excretion of phosphates, oxalate, magnesium and lower urinary pH were found in the group with predominant weddellite stones. A reduced value of each of these 4 variables increased more than sixfold the chance of diagnosing urolithiasis with stone composed of over 60% weddellite. Conclusions The urinary metabolic profile is associated with the composition of renal stones estimated in infrared spectroscopy in children with idiopathic hypercalciuria. The coexistence of several urinary excretion anomalies improves the prediction of the composition of the stones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".