Patient weight and event-free survival for children under 2 years of age at diagnosis with favorable histology Wilms tumor
Bibliographic record
Abstract
20002 Background: Over- and underweight have been associated with excess mortality in certain childhood cancers. The impact of the child’s weight at diagnosis on event-free survival (EFS) in favorable histology Wilms tumor (FH WT) is unknown. Methods: Patients with FH WT under 2 years of age at enrolment on NWTS-5 were included. This age group was analyzed by body weight in kilograms because body mass index (BMI) norms do not exist for individuals less than 2 years old. Outcomes by BMI for children older than 2 years of age with FH WT will be analyzed separately. CDC 2000 growth charts were used. Patients were stratified for risk based on stage and chemotherapy protocol [EE4A = vincristine/dactinomycin] [DD4A = vincristine/doxorubicin/ actinomycin]. A univariate analysis of the relationship of weight-for-age and EFS was calculated. A Cox proportional hazards model was fitted for EFS examining four subsets of weight-for-age by percentiles: a) less than 5%, b) 5–9.9%, c) 90–94.9% and d) more than 95% and adjusting for risk/treatment groups via stratification. Results: 594 patients met the study criteria. 567 had weights recorded. Median follow-up was 4.7 years. 10% of patients had a weight for age percentile of 5.6 or below and 10% had a weight percentile of 94.1 or above. A univariate analysis of the relationship of weight-for-age and EFS showed no relationship (p=0.40, log-rank test). A Cox proportional hazards model, stratified by risk/treatment groups, showed that low or high weight-for-age was not predictive of outcome (p=0.24). Conclusions: There was no evidence that low or high weight-for-age was predictive for EFS among patients less than 2 years old with FH WT. There were more patients with lower or higher weight than would be expected. [Table: see text] No significant financial relationships to disclose.
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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 | 0.001 | 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) | 0.002 | 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".