Health Outcomes in Childhood Cancer Survivors with Congenital Anomalies in the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Congenital anomalies are associated with an increased risk of childhood cancer. However, there is a knowledge gap about health outcomes for childhood cancer survivors with congenital anomalies. METHODS: We included childhood cancer survivors from the Childhood Cancer Survivor Study (n = 22,247), comparing survivors with and without self-reported anomalies. Using Cox regression, we estimated HR and 95% confidence intervals (CI) of chronic health conditions (CHC) classified per the Common Terminology Criteria for Adverse Events from 1 (mild) to 5 (fatal) and subsequent malignant neoplasms (SMN), comparing survivors by anomaly status. We calculated age-, sex-, and calendar year-specific mortality rates and standardized mortality ratios for survivors compared with the US population. RESULTS: Among survivors, 16.9% (n = 3,880) reported a congenital anomaly. Survivors with anomalies had a higher rate of any CHC (grades 1-5: HR, 1.24; 95% CI, 1.18-1.31), severe CHCs (grades 3-5: HR, 1.29; 95% CI, 1.19-1.40), and multiple CHCs of any grade (≥2 CHCs: HR, 1.31; 95% CI, 1.24-1.39; ≥3 CHCs: HR, 1.42; 95% CI, 1.33-1.52). Survivors with anomalies had an increased rate of soft-tissue sarcomas (HR, 1.96; 95% CI, 1.12-3.44). For deaths related to the original cancer diagnosis, survivors with anomalies (compared with those with no anomalies) had a lower mortality rate (0.64 vs. 0.90 per 1,000 person-years). CONCLUSIONS: We identified an increased rate of CHCs and SMNs among childhood cancer survivors with anomalies and lower mortality directly related to the cancer diagnosis. IMPACT: Future work will focus on the evaluation of genetic pathways that increase the risk of CHCs and SMNs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".