Wilms' tumour: a systematic review of risk factors and meta-analysis
Bibliographic record
Abstract
Wilms' tumour comprises 95% of all renal cancers among children less than 15 years of age. The purpose of this review is to examine the existing literature on perinatal and environmental risk factors for Wilms' tumour. A search for epidemiological studies that examined risk factors for Wilms' tumour was undertaken in Medline, LILACS, ISI Web of Science and Dissertation Abstracts. A total of 37 studies, including 14 cohort, 21 case-control and 2 case-cohort studies, were identified that examined environmental and perinatal risk factors. Most studies were from Western Europe and North America, and among case-control studies, 16 used randomly selected population-based controls. We observed a significantly increased risk of Wilms' tumour with maternal exposure to pesticides prior to the child's birth (OR = 1.37 [95% CI 1.09, 1.73]), high birthweight (OR = 1.36 [95% CI 1.12, 1.64]) and preterm birth (OR = 1.44 [95% CI 1.14, 1.81]), although the results regarding pesticide exposure may be subject to publication bias (Egger's test, P = 0.09). Further analyses to adjust for the heterogeneity in the results for high birthweight and preterm birth did not statistically change the significance of the results. Additionally, an increased though not statistically significant risk of Wilms' tumour was associated with maternal hypertension (OR = 1.30 [95% CI 0.99, 1.72]), and, compared with the first born, being a second or later birth was associated with a significantly decreased risk (OR = 0.82 [95% CI 0.71, 0.95]). This review suggests a role for several perinatal and environmental risk factors in the aetiology of Wilms' tumour.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".