Incidence and Outcome of Infants With Cancer in Canada: A Report From Cancer in Young People in Canada Database
Bibliographic record
Abstract
PURPOSE: Infants with cancer are rare and face unique challenges. Our study aims to describe the incidence of infantile cancers in Canada and to compare treatment-related mortality (TRM) and their outcomes with those of older children. METHODS: We conducted a retrospective cohort study using the Cancer in Young People in Canada database, including all infants (0 to <1 year old) with newly diagnosed cancer from 2001 to 2020. Population-based data were used to estimate annual cancer incidence. Cox proportional hazards models were used to compare TRM, event-free survival (EFS) and overall survival (OS) between (1) neonates (<30 days old) and older infants (≥30 days to <1 year old, (2) younger (<6 months) and older infants (≥ 6 months), and (3) all infants with older children (≥1 to ≤10 years old). RESULTS: A total of 2256 infants were included. The incidence was 30.8 per 100,000 infant-years, and this incidence increased with an annual percent change of 1.6%, p < 0.001. Neonates had significantly higher TRM (6.1 vs. 3.6%, HR 2.76, p < 0.001) as well as infants younger than 6 months (5.2 vs. 2.7%, HR 2.05, p < 0.001). Compared with older children, infants experienced higher risk of TRM (4.1 vs. 2.1%, HR 2.45, p < 0.001) and had inferior 5-year EFS (66.2 vs. 68.6%, HR 1.27, p < 0.001) and OS (77.5 vs. 78.6%, HR 1.22, p < 0.001). CONCLUSION: The annual incidence of infantile cancers in Canada has increased and their outcomes are significantly worse than those of older children, as they face a higher risk of TRM, particularly in younger infants. PRÉCIS: The incidence of cancer in infants has increased significantly in Canada between 2001 and 2020 and their outcomes are significantly lower than those of older children, being at greater risk of TRM.
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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 | 0.006 |
| Science and technology studies | 0.001 | 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".