‘Teratoid’ Hepatoblastoma: An Intriguing Variant of Mixed Epithelial-Mesenchymal Hepatoblastoma
Bibliographic record
Abstract
Liver neoplasms are quite rare in childhood. They often involve 6.7 cases per 10 million children aged 18 years or younger. Hepatoblastoma (HB) is the most frequent tumor, but this neoplasm's rarity points essentially to the difficulty of performing biologic studies and large-scale therapeutic trials. On the pathological ground, HB is separated into an entirely epithelial neoplasm or a mixed neoplasm with epithelial and mesenchymal components. This last category has been further subdivided into harboring teratoid features or not. The 'teratoid' HB includes a mixture of components with heterologous origin. The heterologous components include neuroectoderm, endoderm, or melanin-holding cells with or without mesenchymal components. The most important criterium for the teratoid component is neuroepithelium, melanin, and, more recently, a yolk-sac-like component and neuroendocrine components. The mesenchymal components include muscle, osteoid, and cartilage, which are most often observed mainly in 'teratoid' neoplasms. The teratoid component or mesenchymal components are diagnosed with biopsies. They appear more prominent after chemotherapy due to the response and shrinkage of epithelial elements and non- or low-responsive components of mixed HB. This review focuses on the clinical, radiological, and pathological findings of HB with teratoid features.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".