Serum Liver Enzyme Patterns in Pediatric Hepatitis: A Systematic Review
Bibliographic record
Abstract
Patterns of serum aminotransferases, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) offer essential insights into the etiology and severity of pediatric hepatitis. Recent epidemiologic shifts, including adenovirus- and AAV2-associated cases, have highlighted the need for an updated synthesis of biochemical trajectories in children. Objectives: To systematically review published data (2018–2024) describing serum ALT and AST patterns in pediatric hepatitis across classical and emerging etiologies. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science were searched for English-language original studies reporting ALT/AST levels in children with hepatitis. Reviews, meta-analyses, and non-original reports were excluded. Methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) for cohort studies and the Joanna Briggs Institute (JBI) checklists for cross-sectional and case-series designs. Extracted data included study characteristics, population details, enzyme levels, and clinical outcomes. Due to heterogeneity in design and reporting, findings were synthesized narratively. Results: Fourteen studies comprising approximately 2,300 participants were included. Autoimmune hepatitis demonstrated sustained moderate-to-high ALT/AST elevations (300–2,400 U/L). Acute viral hepatitis A/E showed abrupt spikes typically exceeding 1,000 U/L with rapid normalization. Severe or non-A-E hepatitis and adenovirus/AAV2-associated cases displayed the most extreme enzyme surges, with peaks occasionally surpassing 5,000 U/L. Most studies showed moderate overall quality but consistently low measurement bias. Conclusions: Serum ALT and AST remain robust and sensitive markers of pediatric hepatocellular injury, with distinct kinetic profiles across etiologies. Standardized, multicenter studies are needed to refine biochemical thresholds and enhance diagnostic interpretation.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".