Liver Transplantation for Hepatic Epithelioid Hemangioendothelioma: The Canadian Multicentre Experience
Bibliographic record
Abstract
INTRODUCTION: Hepatic epithelioid hemangioendothelioma (HEHE) is a rare entity. At the present time, there is no standardized effective therapy. Liver transplantation (LT) has emerged as a treatment for this rare tumour. OBJECTIVE: To evaluate the outcome of liver transplantation for HEHE at eight centres across Canada. METHODS: The charts of patients who were transplanted for HEHE at eight centres across Canada were reviewed. RESULTS: A total of 11 individuals (eight women and three men) received a LT for HEHE. All LTs were performed between 1991 and 2005. The mean (+/- SD) age at LT was 38.7+/-13 years. One patient had one large liver lesion (17 cm x 14 cm x 13 cm), one had three lesions, one had four lesions and eight had extensive (five or more) liver lesions. One patient had spleen involvement and two had involved lymph nodes at the time of transplantation. The mean duration of follow-up was 78+/-63 months (median 81 months). Four patients (36.4%) developed recurrence of HEHE with a mean time to recurrence of 25+/-25 months (median 15.6 months) following LT. The calculated survival rate following LT for HEHE was 82% at five years. CONCLUSIONS: The results of LT for HEHE are encouraging, with a recurrence rate of 36.4% and a five-year survival rate of 82%. Further studies are needed to help identify patients who would benefit most from LT for this rare tumour.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".