Maternal Exposure to Bisphenol-A and Fetal Growth Restriction: A Case-Referent Study
Bibliographic record
Abstract
We conducted a case-referent study of the effect of exposure to bisphenol-A on fetal growth in utero in full-term, live-born singletons in Alberta, Canada. Newborns <10 percentile of expected weight for gestational age and sex were individually matched on sex, maternal smoking and maternal age to referents with weight appropriate to gestational age. Exposure of the fetus to bisphenol-A was estimated from maternal serum collected at 15-16 weeks of gestation. We pooled sera across subjects for exposure assessment, stratified on case-referent status and sex. Individual 1:1 matching was maintained in assembling 69 case and 69 referent pools created from 550 case-referent pairs. Matched pools had an equal number of aliquots from individual women. We used an analytical strategy conditioning on matched set and total pool-level values of covariates to estimate individual-level effects. Pools of cases and referents had identical geometric mean bisphenol-A concentrations (0.5 ng/mL) and similar geometric standard deviations (2.3-2.5). Mean difference in concentration between matched pools was 0 ng/mL, standard deviation: 1 ng/mL. Stratification by sex and control for confounding did not suggest bisphenol-A increased fetal growth restriction. Our analysis does not provide evidence to support the hypothesis that bisphenol-A contributes to fetal growth restriction in full-term singletons.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".