Environmental phthalates exposure during pregnancy
Bibliographic record
Abstract
Background. A large scientific litterature exists reporting facts and hypothesis about relation between some infant morbidity or dysfunctions, and perinatal exposure to external stress factors including chemical contaminants. Among these, phtalates are known for their endocrine disruption effects while hormone levels are essentials to maintain pregnancy and ensure fetus development. Their wide use in consumer products explain their environmental and human matrix ubiquity. Aims. Verify phthalates exposure levels in pregnant women and identify their sources. Methods. Data are provided by MIREC study (Maternal Infant Research on Environmental Chemicals 2008-2012) where 2000 pregnant women were followed in 10 canadian centres. Eleven urinary metabolites (MBP, MBzP, MCHP, MCPP, MOP, MEHHP, MEHP, MEOHP, MEP, MMP, MNP) from eight phtalates (BNP, BBzP, DCHP, DOP, DEHP, DEP, DMP, DNIP) have been measured during first pregnancy trimester. Results. Detection limit (LOD) varies among phtalates (0,2 à 0,7 ?g/L). Samples proportions > LOD are : 100% for MBP, MEOHP and MEP, 99% for MBzP and MEHHP, 98% for MEHP, and 85% for MCPP. Highest median urinary concentrations, magnitude from 30 to 1 ?g/L, are from DEHP, DEP, DBzP, and DOP : MEP, MBP, MEHHP, MEOHP, MBzP, MEHP, and MCPP. The one from MMP, MCHP, MNP and MOP were not determined because less than 15% of samples were > LOD. A priori identified sources in litterature for DEHP, DEP, DBzP, and DOP are : fragrances, PVC flexible products, deodorants, hair prays and moss and gel, shampoo, soap, nail polish, body lotions, printer ink, and insecticides. Conclusions. Phthalates level measured in canadian pregnant women are generally lower than reported in north america. Knowledge of sources and exposure levels associated with adverse effects will allow to lower pregnancy exposure with appropriate regulation considering fetus health. Identifying high exposure risk groups will be usefull for targeted intervention to ensure infant health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".