Balanced care in managing hepatoblastoma in a patient with trisomy 18: A case report
Bibliographic record
Abstract
Introduction: Despite a high mortality rate during the first year of life, an increasing number of patients with Trisomy 18 (T18) survive into childhood. Offering invasive medical and surgical procedures to this population continues to raise ethical questions. We present the case of a child with T18 and hepatoblastoma, highlighting therapeutic and ethical challenges. Case presentation: A 2.5-year-old female with T18 known for a bicuspid aortic valve and developmental delay presented with an abdominal mass. Alpha-fetoprotein level was elevated (46,540 ng/mL) and imaging showed a single exophytic segment 5–6 liver mass and no metastatic disease, compatible with a PRETEXT II hepatoblastoma. Multidisciplinary discussions took place focusing on treatment options, the patient's quality of life, and her global prognosis. Given the patient's overall good condition, few comorbidities, and the family's wishes to proceed with curative measures, a bisegmentectomy 5,6 was performed. The patient was discharged 12 days postoperatively having suffered no surgical complication. Pathology revealed a mixed epithelial and mesenchymal hepatoblastoma. She received two cycles of adjuvant chemotherapy (cisplatin) without suffering from any complication. Now 5.5 years post-treatment, she remains disease-free. The family is grateful their daughter's chromosomal anomaly did not negatively influence medical and surgical teams in offering her optimal treatment options. Conclusion: Unless suffering from severe underlying medical comorbidities, patients with T18 and hepatoblastoma can receive gold standard care. Multidisciplinary collaboration involving surgeons, medical providers, as well as families are essential to determine optimal individualized treatment strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".