Late Recurrence in Pediatric Cancer: A Report From the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: An increasing percentage of childhood cancer patients are surviving their disease, but there is limited research on late recurrence. We sought to estimate late recurrence rates for the most common pediatric cancers and to determine risk factors for late recurrence. METHODS: The incidence of late recurrences, or first recurrences that occurred more than 5 years after diagnosis, was analyzed for the most common pediatric cancers using data from the Childhood Cancer Survivor Study, a retrospective cohort of 5-year survivors of childhood and adolescent cancers who were diagnosed between 1970 and 1986. A total of 12,795 survivors with no history of recurrence within 5 years after their original cancer diagnosis were included in the analysis, with a total of 217,127 person-years of follow-up. Cumulative incidence of late recurrence at 5, 10, 15, and 20 years after diagnosis was calculated using death as a competing risk. Adjusted relative rates of late recurrence were obtained using multivariable Poisson regression. All statistical tests were two-sided. RESULTS: Overall, 5-year survivors of pediatric cancers experienced a cumulative incidence of recurrent disease of 4.4%, 5.6%, and 6.2% at 10, 15, and 20 years, respectively. Cumulative incidence varied by diagnosis: Survivors of Ewing sarcoma and astrocytoma had the highest 20-year cumulative incidences at 13.0% (95% confidence interval [CI] = 9.4 to 16.5) and 14.4% (95% CI = 12.3 to 16.6), respectively. In multivariable analysis, the greatest risk factors for late recurrence included diagnosis, combination treatment with chemotherapy and radiation, earlier treatment era, and fewer years since diagnosis (P < .001 for all). CONCLUSION: Late recurrence is a risk for some pediatric cancers. By understanding diagnosis-specific risks, patients, families, and their medical providers can be better informed of the probability of cure.
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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 | 0.000 |
| 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".