Cohort Profile: The Maternal‐Infant Research on Environmental Chemicals Research Platform
Bibliographic record
Abstract
BACKGROUND: The Maternal-Infant Research on Environmental Chemicals (MIREC) Study was established to obtain Canadian biomonitoring data for pregnant women and their infants, and to examine potential adverse health effects of prenatal exposure to priority environmental chemicals on pregnancy and infant health. METHODS: Women were recruited during the first trimester from 10 sites across Canada and were followed through delivery. Questionnaires were administered during pregnancy and post-delivery to collect information on demographics, occupation, life style, medical history, environmental exposures and diet. Information on the pregnancy and the infant was abstracted from medical charts. Maternal blood, urine, hair and breast milk, as well as cord blood and infant meconium, were collected and analysed for an extensive list of environmental biomarkers and nutrients. Additional biospecimens were stored in the study's Biobank. The MIREC Research Platform encompasses the main cohort study, the Biobank and follow-up studies. RESULTS: Of the 8716 women approached at early prenatal clinics, 5108 were eligible and 2001 agreed to participate (39%). MIREC participants tended to smoke less (5.9% vs. 10.5%), be older (mean 32.2 vs. 29.4 years) and have a higher education (62.3% vs. 35.1% with a university degree) than women giving birth in Canada. CONCLUSIONS: The MIREC Study, while smaller in number of participants than several of the international cohort studies, has one of the most comprehensive datasets on prenatal exposure to multiple environmental chemicals. The biomonitoring data and biological specimen bank will make this research platform a significant resource for examining potential adverse health effects of prenatal exposure to environmental chemicals.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".