Hepatitis C in children with chronic kidney disease: A single-center, Egypt
Bibliographic record
Abstract
Prevalence of hepatitis C varies largely according to geographical distribution, and Egypt so far has the highest prevalence worldwide. The aim of this study was to evaluate hepatitis C infection in chronic kidney disease (CKD) children in our center with regard to its incidence and other morbidities. This is a cross-sectional study involving 50 children with CKD, not on dialysis. All patients underwent a thorough history taking including disease duration and mean duration of admission, clinical examination including blood pressure measurements, and routine laboratory examination such as hemoglobin level, serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine. The detection of anti-hepatitis C virus (HCV) antibodies was done in all patients based on the use of third-generation enzyme immunoassay (EIA) that detects antibodies directed against various HCV epitopes. Nine (18%) children were found to be hepatitis C positive and 41 were negative to hepatitis C. Infected cases were of older age group and had a longer duration of CKD, lower estimated glomerular filtration rate (eGFR), lower hemoglobin, higher ALT, higher serum urea, and creatinine. We conclude that 18% of children with CKDs have hepatitis C infection, and those with longer the duration of renal disease is more likely to be positive for HCV. Furthermore, HCV infection may predispose to higher deterioration of eGFR, lower hemoglobin level, and more days of admission. We recommend routine testing of HCV in all children with CKD.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".