Risk due to Elevated Uric Acid Levels in Children With Henoch-Schonlein Purpura
Bibliographic record
Abstract
PURPOSE: To compare uric acid levels in children with Henoch-Schonlein purpura (HSP)without nephritis and with renal damage, and at different pathological grades. METHODS: A total of 451 children were enrolled in this study, including 64 with HSP without nephritis and 387 HSP with kidney damage. Age, gender, uric acid, urea, creatinine and cystatin C levels were reviewed. Pathological findings of those with renal impairment were also reviewed. RESULTS: Among the HSP children with renal damage, 44 were grade I, 167 were grade II and 176 were grade III. There were significant differences in age, uric acid, urea, creatinine and cystatin C levels between the two groups (p<0.05, all). Correlation analysis showed that uric acid levels in children with HSP without nephritis were positively correlated with urea and creatinine levels (p<0.05). Uric acid levels in HSP children with renal damage was positively correlated with age, urea, creatinine and cystatin C levels (p<0.05, all). Regression analysis found that, without adding any correction factors, there were significant differences in uric acid levels between the two groups; however, after adjusting for pathological grade, there was no longer a significant difference. CONCLUSIONS: There were significant differences of uric acid levels in children with HSP without nephritis and with renal impairment. Uric acid levels in the renal impairment group were significantly higher than that in the HSP without nephritis group. Uric acid levels were related to only the presence or absence of renal damage, not to the pathological grade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".