Research Article SubsequentMalignant Neoplasms in a Population- Based Cohort of Pediatric Cancer Patients: A Focus on the First 5 Years
Bibliographic record
Abstract
Background: The purpose was to describe the development of subsequent malignant neoplasms (SMN) among a population-based cohort of pediatric cancer patients, with a focus on SMNs that occurred within the first 5 years from diagnosis. Methods: The cohort was identified from POGONIS, an active provincial registry. Cohort members were Ontario residents ages 0 to 14.9 years at primary diagnosis between January 1985 and December 2008. SMNs that developed <18 years were captured by POGONIS, whereas SMNs diagnosed later were identified through linkage. Cumulative incidence and standardized incidence ratios (SIR) were calculated, and proportional hazards models were estimated to examine factors associated with SMN development. Results: A total of 7,920 patients were eligible. 2.4 % (188/ 7,920) developed 197 SMNs.Mean follow-up timewas 10.7 years (SD 7.6 years; range, 0.0–26.4 years) withmean time to SMNof 8.5 years (SD 6.3 years; range, 0.0–24.9 years). The SIR for the development of a SMN was 9.9 [95 % confidence interval (CI), 8.6–11.4]. 40.6 % of SMNs (80/197) developed within 5 years. Early SMNs were more likely to be leukemia and lymphoma. Factors associated with early SMN were primary diagnosis of a bone tumor (OR, 4.88; 95 % CI, 1.52–15.60), exposure to radio-therapy (OR, 1.82; 95 % CI, 1.02–3.22), and the highest dose of epipodophyllotoxin (OR, 3.74; 95 % CI, 1.88–7.42). Conclusions: Over 40 % of SMNs diagnosed in childhood cancer patients occurred in the first 5 years after diagnosis, suggesting a need for early and ongoing surveillance. Impact: The early development of certain SMNs reinforces the need for early and continued surveillance at all stages for pediatric cancer patients. Cancer Epidemiol Biomarkers Prev; 24(10); 1585–92. 2015 AACR.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".