Parental smoking, maternal alcohol consumption during pregnancy and the risk of neuroblastoma in children. A pooled analysis of the ESCALE and ESTELLE French studies
Bibliographic record
Abstract
Neuroblastoma (NB) is the most common extra-cranial tumour in children. Little is known about the aetiology of NB. The early age at onset and the embryonic nature suggest a role for perinatal exposures. We conducted a pooled analysis of two French national population-based case-control studies to explore whether there was an association between parental smoking and alcohol consumption and the risk of NB. The mothers of 357 NB cases and 1,783 controls from general population, frequency matched by age and sex, were interviewed on demographic, socioeconomic and perinatal characteristics, maternal reproductive story, and life-style and childhood environment. Unconditional logistic regression was used to estimate pooled odds ratios and 95% confidence intervals. A meta-analysis of our findings with those of previous studies was also conducted. Maternal smoking during pregnancy was slightly more often reported for the cases (24.1%) than for the controls (19.7%) (OR 1.3 [95% CI 0.9-1.7]; summary OR from meta-analysis 1.1 [95% CI 1.0-1.3]. Paternal smoking in the year before child's birth were not associated with NB as independent exposure (OR 1.1 [95% CI 0.9-1.4] but the association was stronger when both parents reported having smoked during pregnancy (OR 1.5 [95% CI 1.1-2.1]. No association was observed with maternal alcohol intake during pregnancy (OR 1.0 [95% CI 0.8-1.4], summary OR from meta-analysis 1.0 [95% CI 0.9-1.2]. Our findings provide some evidence of an association between maternal smoking during pregnancy and NB and add another reason to recommend that women refrain from smoking during pregnancy.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.032 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".