Penetrance of Mutations in the Familial Wilms Tumor Gene FWT1
Bibliographic record
Abstract
Wilms tumor is an embryonal kidney cancer that affects one in 10000 children. Epidemiologic studies have shown that 1%–3% of cases of Wilms tumor are familial and that a predisposition to Wilms tumor is probably caused by rare germline mutations acting in a dominant fashion (1). The risks of Wilms tumor conferred by mutations in these genes are poorly characterized, with estimates of their penetrance ranging from 18% to 63% (2–4). These estimates represent an average of the risks of all genes that predispose an individual to Wilms tumor and, therefore, are influenced by population-dependent variation in the prevalence of mutations in different genes. Constitutional mutations in the WT1 gene on chromosome 11p13 predispose an individual to Wilms tumor and are associated with genitourinary abnormalities. Germline WT1 mutations have been reported in only four families (three with two cases of Wilms tumor and one with three cases of Wilms tumor) and do not appear to be a common cause of familial predisposition to the disease [reviewed in (5)]. Several genetic syndromes (e.g., the Beckwith–Wiedemann syndrome) have been associated with a predisposition to Wilms tumor, but such syndromes only rarely result in familial cases of the disease [reviewed in (5)].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".