Biomonitoring of Human Fetal Exposure to Environmental Chemicals in Early Pregnancy
Bibliographic record
Abstract
The first trimester of human fetal life, a period of extremely rapid development of physiological systems, represents the most rapid growth phase in human life. Interference in the establishment of organ systems may result in abnormal development that may be manifest immediately or programmed for later abnormal function. Exposure to environmental chemicals may be affecting development at these early stages, and yet there is limited knowledge of the quantities and identities of the chemicals to which the fetus is exposed during early pregnancy. Clearly, opportunities for assessing fetal chemical exposure directly are extremely limited. Hence, this review describes indirect means of assessing fetal exposure in early pregnancy to chemicals that are considered disrupters of development. Consideration is given to such matrices as maternal hair, fingernails, urine, saliva, sweat, breast milk, amniotic fluid and blood, and fetal matrices such as cord blood, cord tissue, meconium, placenta, and fetal liver. More than 150 articles that presented data from chemical analysis of human maternal and fetal tissues and fluids were reviewed. Priority was given to articles where chemical analysis was conducted in more than one matrix. Where correlations between maternal and fetal matrices were determined, these articles were included and are highlighted, as these may provide the basis for future investigations of early fetal exposure. The determination of fetal chemical exposure, at the time of rapid human growth and development, will greatly assist regulatory agencies in risk assessments and establishment of advisories for risk management concerning 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".