Risk of a second cancer in Canadians diagnosed with a first cancer in childhood or adolescence
Bibliographic record
Abstract
BACKGROUND: Second cancers are an adverse outcome experienced by childhood cancer survivors. We quantify the risk and correlates of a second cancer in Canadians diagnosed with a first cancer prior to age 20 years. METHODS: Using death-linked Canadian Cancer Registry data, a population-based cohort diagnosed with a first cancer between 1992 and 2014, prior to age 20 years, were followed for occurrence of a second cancer to the end of 2014. We estimate standardized incidence ratios (SIR), absolute excess risks (AER), cumulative probabilities, and hazard ratios (HR). FINDINGS: 22,635 people contributed 204,309•1 person-years of follow-up. Overall risk of a second cancer was 6•5 (95% CI: 5•8-7•1) times greater than expected resulting in an AER of 16•5 (14•4-18•5) cancers per 10,000 person-years and a 4•8% (3•8%-6•0%) cumulative probability of a second cancer at 22•6 years of follow-up. SIRs decreased with increasing age at diagnosis and time since diagnosis; were larger in more recent calendar periods of diagnosis; and varied by type of first cancer. Large SIRs in the first year after diagnosis and in those diagnosed in 2010-2014 were partly associated with changing registry practices. For the whole cohort, factors associated with the hazard of a second cancer included: being female vs. male [HR = 1•439 (95%CI: 1•179-1•760)]; being diagnosed in 2005-2014 vs. 1992-2004 [2•084 (1•598-2•719)]; having synchronous first cancers [4•814 (2•042-9•509)]; and being diagnosed with certain types of cancer. Factors varied, however, by type of first cancer. INTERPRETATION: Risks of a second cancer are not equally distributed and can be impacted by changes in registry practice and the methods used to define second cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".