Leukemia and Lymphoma Incidence in Children in Alberta, Canada: A Population-Based 22-Year Retrospective Study
Bibliographic record
Abstract
There is a paucity of published literature on the epidemiology of childhood acute leukemias and lymphomas in Canada. This study was designed to describe children and youth (age <20 years) diagnosed with acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), Hodgkin lymphoma (HL), and non-Hodgkin lymphoma (NHL) in Alberta, Canada, during 22 fiscal years. The Alberta Cancer Registry was used to extract data all ALL, AML, HL, and NHL cases diagnosed between April 1, 1982, and March 31, 2004. Population data for Alberta were also obtained. Descriptive statistics and cluster detection tests were used. During 22 years, 525, 117, 257, and 111 children (total = 1010) were diagnosed with ALL, AML, HL, and NHL, respectively. The median ages at diagnosis were 4, 11, 16, and 12 years for ALL, AML, HL, and NHL, respectively. The majority were male for ALL (287/525, 55%), AML (64/117, 55%), and NHL (81/111, 73%), and female for HL (133/257, 52%). The crude rates per 100,000 children were variable, without significant trends, over time and for each diagnosis; the median annual rates, per 100,000 children, were 3.00 (ranging from 1.87 to 3.75) for ALL, 0.62 (ranging from 0.26 to 1.27) for AML, 1.42 (ranging from 0.76 to 2.67) for HL, and 0.54 (ranging from 0.24 to 1.40) for NHL. A few potential spatiotemporal clusters were identified. They are likely due to small number of cases and plausibly clinically insignificant. Overall, childhood leukemia and lymphoma rates in Alberta have remained relatively stable, with no clear epidemiological trends and no significant spatiotemporal clustering. Further investigations are warranted to see if such stability continues and if spatiotemporal patterns arise from longer studies and studies in larger geographic regions with a larger sample size, whilst analyzing for other causal/associated factors, individual susceptibilities, and disease outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".