Epidemiologic Tools to Study the Influence of Environmental Factors on Fecundity and Pregnancy-related Outcomes
Bibliographic record
Abstract
Adverse pregnancy outcomes entail a large health burden for the mother and offspring; a part of it might be avoided by better understanding the role of environmental factors in their etiology. Our aims were to review the assessment tools to characterize fecundity troubles and pregnancy-related outcomes in human populations and their sensitivity to environmental factors. For each outcome, we reviewed the possible study designs, main sources of bias, and their suggested cures. In terms of study design, for most pregnancy outcomes, cohorts with recruitment early during or even before pregnancy allow efficient characterization of pregnancy-related events, time-varying confounders, and in utero exposures that may impact birth outcomes and child health. Studies on congenital anomalies require specific designs, assessment of anomalies in medical pregnancy terminations, and, for congenital anomalies diagnosed postnatally, follow-up during several months after birth. Statistical analyses should take into account environmental exposures during the relevant time windows; survival models are an appropriate approach for fecundity, fetal loss, and gestational duration/preterm delivery. Analysis of gestational duration could distinguish pregnancies according to delivery induction (and possibly pregnancy-related conditions). In conclusion, careful design and analysis are required to better characterize environmental effects on human reproduction.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".