Unraveling the natural history of presymptomatic cystinuria
Bibliographic record
Abstract
PURPOSE OF REVIEW: Servais et al. recently published clinical practice recommendations for the care of cystinuria patients. However, these guidelines were largely based on retrospective data from adults and children presenting with stones. Significant questions remain about the natural history of cystinuria in presymptomatic children. RECENT FINDINGS: We review the natural history of cystinuria in presymptomatic children followed from birth. In total, 130 pediatric patients were assigned putative genotypes based on parental urinary phenotype: type A/A (N = 23), B/B (N = 6), and B/N (N = 101). Stones were identified in 12/130 (4% of A/A, 17% of B/B, and 1% of B/N patients). Type B/B patients had lower cystine excretion than type A/A patients. Although urine cystine/creatinine fell with age, urine cystine/l rose progressively in parallel with the risk of nephrolithiasis. Each new stone was preceded by 6-12 months of urine specific gravity of more than 1.020. However, average urine specific gravity and pH were not different in stone formers vs. nonstone formers, suggesting that intrinsic stone inhibitors or other unknown factors may be the strongest determinants of individual risk. SUMMARY: The current study reviews the clinical evolution of cystinuria in a cohort of children identified by newborn screening, who were categorized by urinary phenotype and followed from birth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".