Infections and the development of childhood acute lymphoblastic leukemia: a population-based study
Bibliographic record
Abstract
An infectious trigger for childhood acute lymphoblastic leukemia is hypothesized and we assessed the association between the rate, type, and critical exposure period for infections and the development of acute lymphoblastic leukemia. We conducted a matched case-control study using administrative databases to evaluate the association between the rate of infections and childhood acute lymphoblastic leukemia diagnosed between the ages of 2-14 years from Ontario, Canada and we used a validated approach to measure infections. In 1600 cases of acute lymphoblastic leukemia, and 16 000 matched cancer-free controls aged 2-14 years, having >2 infections/year increased the odds of childhood acute lymphoblastic leukemia by 43% (odds ratio = 1.43, 95% confidence interval 1.13-1.81) compared to children with ≤0.25 infections/year. Having >2 respiratory infections/year increased odds of acute lymphoblastic leukemia by 28% (odds ratio =1.28, 95% confidence interval 1.05-1.57) compared to children with ≤0.25 respiratory infections/year. Having an invasive infection increased the odds of acute lymphoblastic leukemia by 72% (odds ratio =1.72, 95% confidence interval 1.31-2.26). Having an infection between the age of 1-1.5 years increased the odds of acute lymphoblastic leukemia by 20% (odds ratio = 1.20, 95% confidence interval 1.04-1.39). Having more infections increased the odds of developing childhood acute lymphoblastic leukemia and having an infection between the ages of 1-1.5 years increased the odds of childhood acute lymphoblastic leukemia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".