Anthropomorphic measurements and event‐free survival in patients with favorable histology Wilms tumor: A report from the Children's Oncology Group
Bibliographic record
Abstract
PURPOSE: We retrospectively examined the effect of body weight and body mass index (BMI) on event-free survival (EFS) of children with Wilms tumor treated on National Wilms Tumor Study-5 (NWTS-5). PATIENTS AND METHODS: Eligible study participants: stages I-IV favorable histology Wilms tumor with immediate nephrectomy; height and weight recorded at diagnosis, and loss of heterozygosity for chromosomes 1p and 16q assessed. RESULTS: A total of 1,532 patients were included in the analysis. The median follow-up was 4.9 years. 493 patients were less than 2 years of age and 1039 were 2 years of age or older. In both age groups there were more patients than expected with a weight or body mass index (BMI) less than the 10 per thousand or greater than the 90 per thousand. There was no relationship of weight-for-age or BMI-for-age and EFS in univariate analyses (P = 0.28, log-rank test for both comparisons). A Cox proportional hazards model, stratified by risk/treatment groups, showed that, among patients less than 2 years of age, low or high weight-for-age was not predictive of EFS (P = 0.16). Similarly, a Cox proportional hazards model, stratified by risk/treatment groups, showed that among patients greater than 2 years of age, low or high body mass index for age was not predictive of EFS (P = 0.58). CONCLUSIONS: There was no evidence that anthropomorphic data obtained at diagnosis for patients with favorable histology stages I-IV Wilms tumor was predictive for EFS in the setting of current treatment regimens. There were more patients with lower or higher weight/BMI than expected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".