Update on Surveillance Guidelines in Emerging Wilms Tumor Predisposition Syndromes
Bibliographic record
Abstract
Wilms tumors are commonly associated with predisposition syndromes. Many of these syndromes are associated with specific phenotypic features and are discussed in the related article from the AACR Pediatric Cancer Working Group. Guidelines for surveillance in this population were published in 2017, but since then several studies have identified new genes with recurrent pathogenic variants associated with increased risk for Wilms tumor development. In general, variants in these genes are less likely to be associated with other phenotypic features. Recently, members of the AACR Pediatric Cancer Working Group met to update surveillance guidelines for patients with a predisposition to Wilms tumors with a review of recently published evidence and risk estimates. Risk estimates for Wilms tumor for the more recently described genes are discussed here along with suggested surveillance guidelines for these populations. Several other emerging clinical scenarios associated with Wilms tumor predisposition are also discussed, including patients with family histories of Wilms tumor and no identified causative gene, patients with bilateral tumors, and patients with somatic mosaicism for chromosome 11p15.5 alterations. This perspective serves to update pediatric oncologists, geneticists, radiologists, counselors, and other health care professionals on emerging evidence and harmonize updated surveillance recommendations in the North American and Australian context for patients with emerging forms of Wilms tumor predisposition.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".