Fetal growth and childhood acute lymphoblastic leukemia: Findings from the childhood leukemia international consortium
Bibliographic record
Abstract
Positive associations have been reported between the measures of accelerated fetal growth and risk of childhood acute lymphoblastic leukemia (ALL). We investigated this association by pooling individual-level data from 12 case-control studies participating in the Childhood Leukemia International Consortium. Two measures of fetal growth-weight-for-gestational-age and proportion of optimal birth weight (POBW)-were analysed. Study-specific odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression, and combined in fixed effects meta-analyses. Pooled analyses of all data were also undertaken using multivariable logistic regression. Subgroup analyses were undertaken when possible. Data on weight for gestational age were available for 7,348 cases and 12,489 controls from all 12 studies and POBW data were available for 1,680 cases and 3,139 controls from three studies. The summary ORs from the meta-analyses were 1.24 (95% CI: 1.13, 1.36) for children who were large for gestational age relative to appropriate for gestational age, and 1.16 (95% CI: 1.09, 1.24) for a one-standard deviation increase in POBW. The pooled analyses produced similar results. The summary and pooled ORs for small-for-gestational-age children were 0.83 (95% CI: 0.75, 0.92) and 0.86 (95% CI: 0.77, 0.95), respectively. Results were consistent across subgroups defined by sex, ethnicity and immunophenotype, and when the analysis was restricted to children who did not have high birth weight. The evidence that accelerated fetal growth is associated with a modest increased risk of childhood ALL is strong and consistent with known biological mechanisms involving insulin-like growth factors. © 2013 UICC.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".