Wilms tumour
Bibliographic record
Abstract
Wilms tumour (WT) is a childhood embryonal tumour that is paradigmatic of the intersection between disrupted organogenesis and tumorigenesis. Many WT genes play a critical (non-redundant) role in early nephrogenesis. Improving patient outcomes requires advances in understanding and targeting of the multiple genes and cellular control pathways now identified as active in WT development. Decades of clinical and basic research have helped to gradually optimize clinical care. Curative therapy is achievable in 90% of affected children, even those with disseminated disease, yet survival disparities within and between countries exist and deserve commitment to change. Updated epidemiological studies have also provided novel insights into global incidence variations. Introduction of biology-driven approaches to risk stratification and new drug development has been slower in WT than in other childhood tumours. Current prognostic classification for children with WT is grounded in clinical and pathological findings and in dedicated protocols on molecular alterations. Treatment includes conventional cytotoxic chemotherapy and surgery, and radiation therapy in some cases. Advanced imaging to capture tumour composition, optimizing irradiation techniques to reduce target volumes, and evaluation of newer surgical procedures are key areas for future research. Wilms tumour (WT) is the most common renal tumour in infants and young children. This Primer reviews the epidemiology, mechanisms, diagnosis and management of WT. In addition, the authors outline potential opportunities to translate novel biological targets to improve clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".