Conditional survival in pediatric malignancies: Analysis of data from the Childhood Cancer Survivor Study and the Surveillance, Epidemiology, and End Results Program
Bibliographic record
Abstract
BACKGROUND: Long-term survivors of pediatric cancer are at risk of life-threatening late effects of their cancer. Previous studies have shown excesses in long-term mortality within high-risk groups defined by demographic and treatment characteristics. METHODS: To investigate conditional survival in a pediatric cancer population, the authors performed an analysis of conditional survival in the original Childhood Cancer Survivor Study (CCSS) cohort and the Surveillance, Epidemiology, and End Results (SEER) database registry. The overall probability of death for patients at 5 years and 10 years after they survived 5, 10, 15, and 20 years since cancer diagnosis and cause-specific death in 10 years for 5-year survivors were estimated using the cumulative incidence method. RESULTS: Among patients in the CCSS and SEER cohorts who were alive 5 years after their cancer diagnosis, within each diagnosis group at least 92% were alive in the subsequent 5 years, except for patients with leukemia, of whom only 88% of 5-year survivors remained alive in the subsequent 5 years. The probability of all-cause mortality in the next 10 years among patients who survived at least 5 years after diagnosis was 8.8% in CCSS and 10.6% in SEER, approximately 75% of which was due to neoplasms as the cause of death. CONCLUSIONS: The risk of death among survivors of pediatric cancer in 10 years can vary between diagnosis groups by at most 12%, even up to 20 years after diagnosis. This information is clinically significant when counseling patients regarding their conditional survival, particularly when survivors are seen in long-term follow-up.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".