Surveillance for Wilms tumour in at-risk children: pragmatic recommendations for best practice
Bibliographic record
Abstract
BACKGROUND: Most Wilms tumours occur in otherwise healthy children, but a small proportion occur in children with genetic syndromes associated with increased risks of Wilms tumour. Surveillance for Wilms tumour has become widespread, despite a lack of clarity about which children are at increased risk of these tumours and limited evidence of the efficacy of screening or guidance as to how screening should be implemented. METHODS: The available literature was reviewed. RESULTS: The potential risks and benefits of Wilms tumour surveillance are finely balanced and there is no clear evidence that screening reduces mortality or morbidity. Prospective evidence-based data on the efficacy of Wilms tumour screening would be difficult and costly to generate and are unlikely to become available in the foreseeable future. CONCLUSIONS: The following pragmatic recommendations have been formulated for Wilms tumour surveillance in children at risk, based on our review: (1) Surveillance should be offered to children at >5% risk of Wilms tumour. (2) Surveillance should only be offered after review by a clinical geneticist. (3) Surveillance should be carried out by renal ultrasonography every 3-4 months. (4) Surveillance should continue until 5 years of age in all conditions except Beckwith-Wiedemann syndrome, Simpson-Golabi-Behmel syndrome and some familial Wilms tumour pedigrees where it should continue until 7 years. (5) Surveillance can be undertaken at a local centre, but should be carried out by someone with experience in paediatric ultrasonography. (6) Screen-detected lesions should be managed at a specialist centre.
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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 | 0.060 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".