Meeting Report: Atmospheric Pollution and Human Reproduction
Bibliographic record
Abstract
BACKGROUND: There is a growing body of epidemiologic literature reporting associations between atmospheric pollutants and reproductive outcomes, particularly birth weight and gestational duration. OBJECTIVES: The objectives of our international workshop were to discuss the current evidence, to identify the strengths and weaknesses of published epidemiologic studies, and to suggest future directions for research. DISCUSSION: Participants identified promising exposure assessment tools, including exposure models with fine spatial and temporal resolution that take into account time-activity patterns. More knowledge on factors correlated with exposure to air pollution, such as other environmental pollutants with similar temporal variations, and assessment of nutritional factors possibly influencing birth outcomes would help evaluate importance of residual confounding. Participants proposed a list of points to report in future publications on this topic to facilitate research syntheses. Nested case-control studies analyzed using two-phase statistical techniques and development of cohorts with extensive information on pregnancy behaviors and biological samples are promising study designs. Issues related to the identification of critical exposure windows and potential biological mechanisms through which air pollutants may lead to intrauterine growth restriction and premature birth were reviewed. CONCLUSIONS: To make progress, this research field needs input from toxicology, exposure assessment, and clinical research, especially to aid in the identification and exposure assessment of feto-toxic agents in ambient air, in the development of early markers of adverse reproductive outcomes, and of relevant biological pathways. In particular, additional research using animal models would help better delineate the biological mechanisms underpinning the associations reported in human studies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".