Second Malignant Neoplasms in Five-Year Survivors of Childhood Cancer: Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Because survival rates among childhood cancer patients are increasing, assessing the risk of second and subsequent malignant neoplasms (SMNs) is ever more important. Using the Childhood Cancer Survivor Study cohort, we identified the risk of SMNS: METHODS: A retrospective cohort of 13 581 children diagnosed with common cancers before age 21 years and surviving at least 5 years was constructed with the use of data from patients treated at 25 U.S. and Canadian institutions. SMNs were ascertained through self-administered questionnaires and verified by pathology reports. Information on therapeutic exposures was abstracted from medical records. The risk of SMN was evaluated by standardized incidence ratios (SIRs) and excess absolute risk. Poisson multiple regression models were used to assess the impact of host and therapy factors on the risk of developing SMNS: All statistical tests were two-sided. RESULTS: In 298 individuals, 314 SMNs were identified (SIR = 6.38; 95% confidence interval [CI] = 5.69 to 7.13). The largest observed excess SMNs were bone and breast cancers (SIR = 19.14 [95% CI = 12.72 to 27.67] and SIR = 16.18 [95% CI = 12.35 to 20.83], respectively). A statistically significant excess of SMNs followed all childhood cancers. In multivariate regression models adjusted for therapeutic radiation exposure, SMNs of any type were independently associated with female sex (P<.001), childhood cancer at a younger age (P for trend <.001), childhood Hodgkin's disease or soft-tissue sarcoma (P<.001 and P =.01, respectively), and exposure to alkylating agents (P for trend =.02). Twenty years after the childhood cancer diagnosis, the cumulative estimated SMN incidence was 3.2%. However, only 1.88 excess malignancies occurred per 1000 years of patient follow-up. CONCLUSIONS: Success in treating children with cancer should not be overshadowed by the incidence of SMNS: However, patients and health-care providers must be aware of risk factors for SMNs so that surveillance is focused and early prevention strategies are implemented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".