Is meconium useful to predict fetal exposure to organochlorines and hydroxylated PCBs?
Bibliographic record
Abstract
The objective of this study is to compare meconium and maternal serum as biomarkers of fetal exposure to organochlorines (OCs). A subset of 40 meconium samples and complementary maternal sera from the Northern Norway Mother-and-Child Contaminant Cohort Study (MISA) were selected. Meconium samples were collected at the earliest opportunity (median 9.0 hours postpartum, range 0-61) and maternal serum in the 2nd trimester (median 19.0 gestational weeks, range 13-34) and analysed for OC contaminants selected from the Arctic Monitoring and Assessment Programme's (AMAP) suite of OCs and selected hydroxylated metabolites. Eight compounds with detection frequencies ≥70% in both media (criterion for inclusion) were included in the statistical analyses. Median concentration ratios for p,p'-DDE, HCB, trans-nonachlor and cis-nonachlor favoured meconium, and PCB 138 and 153 and OH-PCB 146 and 172 were higher in maternal serum. All inter-media correlations were significant (Spearman's rho) for wet-weight concentrations and improved when concentrations in a small subset of 15 meconium and serum samples were both lipid-adjusted; only OH-PCB 146 slightly favoured maternal serum. Multivariable linear regression modelling confirmed that maternal serum was the most consistent predictor of meconium concentrations, with gestational age and time of meconium sampling improving the models. Although more challenging to analyse, the lipid-adjusted OC concentration in meconium is viewed as a sensitive and informative fetal exposure index when taking into account the gestational age and its postpartum sampling time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".