Treatment intensity and risk of chronic health conditions and late mortality among long-term survivors of Wilms tumor: A report from the Childhood Cancer Survivor Study.
Bibliographic record
Abstract
10553 Background: Refinement in risk stratification has led to intensification of therapy for Wilms tumor (WT) patients with adverse prognostic factors. Chronic health conditions (CHCs) including cardiac conditions, subsequent malignant neoplasms (SMNs), and late mortality are known risks for WT survivors, however the impact of specific treatment regimens on these outcomes is largely unknown. Methods: Late mortality (all-cause and non-recurrence death > 5 years from diagnosis), SMNs, and severity-graded CHCs (2 = moderate, 3 = severe, 4 = life-threatening, 5 = fatal) were assessed in 5-year WT survivors in the Childhood Cancer Survivor Study diagnosed from 1970-99. Survivors were categorized according to therapy received (Table). Cumulative incidence of mortality and standard mortality ratios (SMR) were estimated. Piecewise exponential models estimated rate ratios (RR) with 95% confidence intervals (CI). Results: Among 1507 survivors (median age at follow-up 26 yrs; range 6-55), 35-year cumulative incidence of all-cause mortality was 7.9% (SMR 2.9, CI 2.3-3.6) and 5.1% (SMR 1.9, CI 1.4-2.4) for non-recurrence mortality. RRs for developing any grade 2-5 CHC, grade 3-5 SMN, and grade 2-5 cardiac CHCs were higher for survivors compared to sibling controls (2.0, CI 1.8-2.3; 7.4, CI 5.0-10.8; 2.6, CI 2.2-3.1, respectively). Compared with VA and no RT, RR for non-recurrence late mortality and CHCs among survivors were higher for VAD + any RT, and for ≥ 4 drugs + any RT (Table). Conclusions: Administering increased-intensity therapy for WT is associated with increased late health consequences and non-recurrence late mortality, necessitating strategies to monitor and improve long-term health among survivors. [Table: see text]
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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) | 0.001 | 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".