Second malignancies after childhood noncentral nervous system solid cancer: Results from 13 cancer registries
Bibliographic record
Abstract
Children diagnosed with noncentral nervous system solid cancers (NCNSSC) experience several adverse late effects, including second malignant neoplasm. The aim of our study was to assess the risk of specific second malignancies after a childhood NCNSSC. Diagnosis and follow-up data on 10,988 cases of NCNSSC in children (0-14 years) were obtained from 13 registries. Standardized incidence ratios (SIRs) with 95% confidence intervals (CI) and cumulative incidence of second malignancies were computed. We observed 175 second malignant neoplasms, yielding a SIR of 4.6, 95% CI: 3.9-5.3. When considering second cancers with at least 10 occurrences, highest relative risks were found for second malignant bone tumors (SIR = 26.4, 16.6-40.0), soft tissue sarcomas (SIR = 14.1, 6.7-25.8) and myeloid leukemia (SIR = 12.7, 6.3-22.8). Significant increased risks for all malignancies combined were observed after sympathetic nervous system tumors (SIR = 11.4, 5.2-21.6), retinoblastomas (SIR = 7.3, 5.4-9.8), renal tumors (SIR = 5.7, 3.8-8.0), malignant bone tumors (SIR = 5.6, 3.7-8.2), soft tissue sarcomas (SIR = 4.7, 3.2-6.8), germ-cell, trophoblastic and other gonadal neoplasms (SIR = 2.5, 1.1-4.9), carcinomas and other malignant epithelial neoplasms (SIR = 2.2, 1.4-3.3). The highest risk of a second malignancy of any type occurred 5 to 9 years after NCNSSC (SIR = 9.9, 6.8-13.9). The cumulative incidence of second malignancies 10 years after the first neoplasm was eight times higher among NCNSSC survivors than in the general population, with the absolute difference between observed and expected cumulative incidence still increasing after 50 years of follow-up. Children who survived a NCNSSC experience a large increased risk of developing a new malignancy, even many years after their initial diagnosis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".