Advances in Wilms Tumor Treatment and Biology: Progress Through International Collaboration
Bibliographic record
Abstract
Clinical trials in Wilms tumor (WT) have resulted in overall survival rates of greater than 90%. This achievement is especially remarkable because improvements in disease-specific survival have occurred concurrently with a reduction of therapy for large patient subgroups. However, the outcomes for certain patient subgroups, including those with unfavorable histologic and molecular features, bilateral disease, and recurrent disease, remain well below the benchmark survival rate of 90%. Therapy for WT has been advanced in part by an increasingly complex risk-stratification system based on patient age; tumor stage, histology, and volume; response to chemotherapy; and loss of heterozygosity at chromosomes 1p and 16q. A consequence of this system has been the apportionment of patients into such small subgroups that only collaboration between large international WT study groups will support clinical trials that are sufficiently powered to answer challenging questions that move the field forward. This article gives an overview of the Children's Oncology Group and International Society of Pediatric Oncology approaches to WT and focuses on four subgroups (stage IV, initially inoperable, bilateral, and relapsed WT) for which international collaboration is pressing. In addition, biologic insights resulting from collaborative laboratory research are discussed. A coordinated expansion of international collaboration in both clinical trials and laboratory science will provide real opportunity to improve the treatment and outcomes for children with renal tumors on a global level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".