Maternal‐infant biomonitoring of environmental chemicals: The epidemiologic challenges
Bibliographic record
Abstract
There is growing concern about the potential health effects of exposure to various environmental chemicals during pregnancy and infancy. One of the key limitations of past epidemiologic research in this field has been the potential for exposure misclassification to lead to biases in the health risk estimate. The use of biomarkers in pregnancy cohort or case-control studies has significantly advanced the field; however, this is true only if the biomarker is a true measurement of exposure for the relevant time period of interest. There are a number of theoretical and practical constraints to their use, including difficulty interpreting biomonitoring data, high analytical and collection costs, potential participant selection biases, and ethical challenges in reporting results to study subjects. Identifying a representative sample and collecting biospecimens in the developmental window of interest can be problematic. Various strategies for identifying pregnant women range from the more representative but least efficient sampling of the general population to recruitment through early ultrasound clinics and local advertising. Whereas measurement of xenobiotic chemicals in cord blood, amniotic fluid, or meconium provides unequivocal evidence that the chemical has entered the fetal environment, analysis of maternal blood and urine can be used as a surrogate for fetal exposure. Use of stored midpregnancy serum collected for fetal screening and of large-cohort biobanks offer unique opportunities for biomonitoring data for birth defects studies. Future research is needed to explore less invasive matrices for biomonitoring of infants and to develop less costly analytical methods that require smaller sample volumes.
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".