Original Contribution Risk of Childhood Leukemia Associated with Vaccination, Infection, and Medication Use in Childhood The Cross-Canada Childhood Leukemia Study
Bibliographic record
Abstract
Current hypotheses consonant with the peak in leukemia incidence in early childhood point to an infectious etiology. The authors examined the effect of postnatal exposures predicted to affect early immune functioning, including childhood vaccinations, illness, medication use, and breastfeeding patterns. Children 0–15 years of age diagnosed with leukemia from 1990 to 1994 and resident within principal cities across Canada were eligible for inclusion. Through pediatric oncology centers and population-based cancer registries, 399 cases were ascertained at the time of diagnosis. For each participating case, an age-, gender-, and area-matched control was randomly selected from government health insurance rolls. Risk factor information was obtained through personal interviews with each child’s parents or guardians. Conditional logistic regression was used to calculate odds ratios, with adjustment for potential confounders. Use of immunosuppressant medication by the index child led to a deficit of risk (odds ratio 0.37, 95 % confidence interval: 0.16, 0.84), while vitamin intake was positively associated with leukemia (odds ratio 1.66, 95 % confidence interval: 1.18, 2.33). Breastfeeding for more than 6 months was also protective (p < 0.05). Results persisted for cases diagnosed with acute lymphoblastic leukemia and for children diagnosed at 1–5 years of age. These findings suggest a role for early immunologic challenge in the expression of childhood leukemia. breast feeding; case-control studies; infection; leukemia, lymphocytic, acute, L1; risk factors; vaccination
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".