Role of radiotherapy and chemotherapy in the risk of leukemia after childhood cancer: An international pooled analysis
Bibliographic record
Abstract
Childhood cancer survivors are at increased risk for second primary leukemia (SPL), but there is little consensus on the magnitude of some risk factors because of the small size of previous studies. We performed a pooled analysis of all published studies with detailed treatment data, including estimated active bone marrow (ABM) dose received during radiation therapy and doses of specific chemotherapeutic agents for childhood cancer diagnosed from 1930 through 2000, in order to more thoroughly investigate treatment-related risks of SPL. A total of 147 SPL cases (of which 69% were acute myeloid leukemia [AML]) were individually matched to 522 controls, all from four case-control studies including patients from six countries (France, United Kingdom, United States, Canada, Italy and Netherlands). Odds ratios (OR) and corresponding 95% confidence intervals (CIs) were calculated using conditional logistic regression, and the excess OR per Gray (EOR/Gy) was also calculated. After accounting for the other therapies received, topoisomerase II inhibitor was associated with an increased SPL risk (highest tertile vs none: OR = 10.0, 95% CI: 3.7-27.3). Radiation dose to the ABM was also associated with increased SPL risk among those not receiving chemotherapy (EOR/Gy = 1.6, 95% CI: 0.1-14.3), but not among those who received chemotherapy (CT). SPL were most likely to occur in the first decade following cancer treatment. Results were similar when analyses were restricted to AML. The evidence of interaction between radiation and CT has implications for leukemogenic mechanism. The results for topoisomerase II inhibitors are particularly important given their increasing use to treat childhood cancer.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".