Epidemiology of Pediatric Tumors in Quebec: A 17-Year Report of Cancer in Young People in the Canada Registry
Bibliographic record
Abstract
Background. Cancer is the leading cause of disease-related death among children of more than 1 year of age. However, childhood cancer risk factors and etiology are yet to be fully understood. The goal of this study is to identify geographic variation among children and adolescents diagnosed with pediatric tumors between 2001 and 2018 in the province of Quebec. Methods. We analyzed pediatric patients less than 15 years of age from the Cancer in Young People in Canada (CYP-C) surveillance system who were diagnosed between 2001 and 2018 with cancer in the province of Quebec. The age-standardized age-adjusted incidence rates (AAIR) per 100,000 person years were calculated for all childhood cancers by cancer subgroups, Quebec Health regions, and age groups. Results. Overall, 3904 pediatric patients less than 15 years old were diagnosed with cancer in the province of Quebec in 2001–2018. The overall incidence rate (IR) in the province of Quebec was 16.14 (95%CL [15.56–16.73]) per 100,000 person years. For childhood cancers, regions that presented a higher AAIR were Chaudière-Appalaches and Capitale-Nationale with 18.2 and 17.5 per 100,000 person years, respectively. The incidence rates (IRs) in Chaudière-Appalaches (95% CI 1.0439–1.3532) and in Capitale-Nationale (95% CI 1.0124–1.2942) were statistically higher than the incidence in the province of Quebec (p = 0.0090 and p = 0.0310, respectively). When comparing the AAIR of the CNS tumor subgroup in Chaudière-Appalaches and in Capitale-Nationale, with the provincial average, we noticed a statistically higher incidence in Chaudière-Appalaches and a trend for Capitale-Nationale (p < 0.0001 and p = 0.0602, respectively). Conclusion. There is evidence of spatial clusters in Chaudière-Appalaches and Capitale-Nationale as areas for all childhood cancers. Further studies should be performed to investigate potential risk factors in these regions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| 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) | 0.003 | 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".