Late Mortality Among Survivors of Childhood Cancer in Canada: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Children with cancer face an increased risk of complications and death beyond the 5-year survival mark. National surveillance efforts facilitate the systematic tracking of long-term health outcomes, including treatment-related complications and late mortality, among childhood cancer survivors. We aimed to describe the population of 5-year childhood cancer survivors in Canada, quantify the risk of death among survivors relative to the general population, and identify characteristics associated with late mortality. METHODS: This retrospective cohort study used the Canadian Cancer Registry linked to the Canadian Vital Statistics-Death database (excludes Quebec). Survivors were diagnosed with cancer before 15 years old (1992-2012) and still alive five years after diagnosis. We approximated the risk of late mortality relative to the general population using standardized mortality ratios (SMRs) and absolute excess ratios (AERs). Cumulative all-cause and cause-specific mortality and time-to-event models identified characteristics associated with late mortality. RESULTS: Of the 10,800 5-year survivors, 405 (4%) had a late death by 2017 (median follow-up: 9.1 years). Cancer recurrence or progression caused most late deaths (64%), followed by subsequent primary neoplasms (11%) and other health-related causes (15%). Survivors had a higher risk of all-cause mortality than the general population (SMR = 9.4; 95% CI = 8.5-10.4; AER = 34.8, 95% CI = 30.8-38.8). Risk was highest in the first 5-9 years of follow-up. Cumulative mortality differed significantly by age at diagnosis, sex and cancer type. INTERPRETATION: Our results underline the importance of long-term surveillance of childhood cancer survivors, as mortality rates remain higher than the general population for at least two decades after diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.001 | 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".