The pediatric medical liver biopsy: indications, procedures, and histopathology
Bibliographic record
Abstract
INTRODUCTION: Investigative work in pediatric liver disease is rapidly growing. Despite noninvasive methods in place for several years, the medical liver biopsy is still a critical step for diagnosing and treating pediatric patients. We provide a narrative review focusing on indications, procedures, and histopathology of pediatric liver biopsy. AREAS COVERED: We searched the PubMed, Scopus, and Cochrane databases for articles on pediatric liver biopsy, discussing the clinical implications of several procedures and their associated costs. We also complemented our search by digging into the gray literature (e.g., reports, abstracts, textbooks, and Google Scholar) for similar items. The search for articles was conducted between 1 January 2022, and 31 July 2025. EXPERT OPINION: Pediatric liver biopsy is not limited to data gathered following formalin-fixation and paraffin-embedded liver tissue. It includes frozen tissue-based special stains, such as the oil red-O stain for lipids; a specimen devoted to ultrastructural analysis using a transmission electron microscope; and a flash-frozen tissue specimen used for future transcriptomics and genomic studies (e.g., single-nucleotide polymorphisms).
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".