A systematic review and meta-analysis of the association between childhood infections and the risk of childhood acute lymphoblastic leukaemia
Bibliographic record
Abstract
BACKGROUND: To determine whether childhood infections were associated with the development of childhood acute lymphoblastic leukaemia (ALL). METHODS: We included studies that assessed any infection in childhood prior to the diagnosis of ALL in children aged 0-19 years compared to children without cancer. The primary analysis synthesised any infection against the odds of ALL, and secondary analyses assessed the frequency, severity, timing of infections, and specific infectious agents against the odds of ALL. Subgroup analyses by data source were investigated. RESULTS: In our primary analysis of 12 496 children with ALL and 2 356 288 children without ALL from 38 studies, we found that any infection was not associated with ALL (odds ratio (OR)=1.10, 95% CI: 0.95-1.28). Among studies with laboratory-confirmed infections, the presence of infections increased the odds of ALL by 2.4-fold (OR=2.42, 95% CI: 1.54-3.82). Frequency, severity, and timing of infection were not associated with ALL. CONCLUSIONS: The hypothesis put forward by Greaves and others about an infectious aetiology are neither confirmed nor refuted and the overall evidence remains inadequate for good judgement. The qualitative difference in the subgroup effects require further study, and future research will need to address the challenges in measuring infectious exposures.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".