Assessing preconception exposure to environmental chemicals and fecundity: Strategies, challenges, and research priorities
Bibliographic record
Abstract
In 2022, approximately one out of six people globally experienced infertility at some point in their life. Environmental chemicals, particularly those with endocrine disrupting activity, may contribute to impaired fecundity and infertility. We review existing prospective cohort studies of environmental chemicals and fecundity, identify methodological challenges and biases, and outline future research priorities. Studies of preconception environmental chemical exposures and fecundity have occurred in US, Singapore, China and Denmark with recruitment as early as 1982-1986, as recent as 2015-2017 and sample sizes ranging from 99 to 936. Higher exposure to certain chemicals (e.g. heavy metals, perfluoroalkyl substances) was associated with longer time to pregnancy; yet the literature is scarce or nonexistent for many chemicals. Furthermore, prospective studies face challenges and potential biases related to recruiting participants prior to conception, measuring environmental chemicals during critical windows of exposure, and ascertaining when pregnancy occurred. Research priorities include expanding the scope of biomonitoring data collected during the preconception period, continuing to develop and validate analytic methods for self-sampled biospecimens in traditional and novel matrices, collecting data in male partners and investigating etiologic associations according to indicators of marginalization.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".