Management of undifferentiated embryonal sarcoma of the liver: A Pediatric Surgical Oncology Research Collaborative study
Bibliographic record
Abstract
Abstract Background Undifferentiated embryonal sarcoma of the liver (UESL) is a rare tumor for which there are few evidence‐based guidelines. The aim of this study was to define current management strategies and outcomes for these patients using a multi‐institutional dataset curated by the Pediatric Surgical Oncology Research Collaborative. Methods Data were collected retrospectively for patients with UESL treated across 17 children's hospitals in North America from 1989 to 2019. Factors analyzed included patient and tumor characteristics, PRETEXT group, operative details, and neoadjuvant/adjuvant regimens. Event‐free and overall survival (EFS, OS) were the primary and secondary outcomes, respectively. Results Seventy‐eight patients were identified with a median age of 9.9 years [interquartile range [IQR): 7–12]. Twenty‐seven patients underwent resection at diagnosis, and 47 patients underwent delayed resection, including eight liver transplants. Neoadjuvant chemotherapy led to a median change in maximum tumor diameter of 1.6 cm [IQR: 0.0–4.4] and greater than 90% tumor necrosis in 79% of the patients undergoing delayed resection. R0 resections were accomplished in 63 patients (81%). Univariate analysis found that metastatic disease impacted OS, and completeness of resection impacted both EFS and OS, while multivariate analysis revealed that R0 resection was associated with decreased expected hazards of experiencing an event [hazard ratio (HR): 0.14, 95% confidence interval (CI): 0.04–0.6]. At a median follow‐up of 4 years [IQR: 2–8], the EFS was 70.0% [95% CI: 60%–82%] and OS was 83% [95% CI: 75%–93%]. Conclusion Complete resection is associated with improved survival for patients with UESL. Neoadjuvant chemotherapy causes minimal radiographic response, but significant tumor necrosis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".