Phthalate exposure and subfecundity in preconception couples: A nested case-control study
Bibliographic record
Abstract
BACKGROUND: Phthalates (PAEs) are endocrine-disrupting chemicals ubiquitously found in the environment. This study aimed to examine the association between exposure of PAEs and subfecundity in preconception couples. METHODS: This is a nested case-control study based on preconception cohort. Preconception couples with intention to conceive were enrolled and followed up until a clinically confirmed pregnancy or 12 menstrual cycles of preparation for conception. A total of 107 couples with subfecundity- time to pregnancy (TTP) more than 12 menstrual cycles, and 144 couples ≤12 cycles were included in the analysis. The levels of PAE metabolites in one spot urine samples were detected and compared between the groups. The weighted quantile sum (WQS) regression model and Bayesian kernel machine regression (BKMR) model were used to examine the joint effects of couples' exposure to PAEs on subfecundity. RESULTS: Using the multivariate binary logistic regression model, compared to the lowest quartile of urinary ∑PAEs concentration group, both preconception females (aOR=2.42, 95% CI: 1.10-5.30, p=0.027) and males (aOR=2.99, 95% CI: 1.36-6.58, p=0.006) in the highest quartile group had an increased risk of subfecundity, and a dose-response relationship was observed between PAEs and the risk of subfecundity. The WQS analyses found that co-exposure to PAE mixture was a risk factor for subfecundity in preconception female (aOR=1.76, 95% CI: 1.38-2.26, p<0.001), male (aOR=1.58, 95% CI: 1.20-2.08, p=0.001), and couple (aOR=2.39, 95% CI: 1.61-3.52, p<0.001). The BKMR model found a positive combined effect of mixed exposure to PAEs on the risk of subfecundity. CONCLUSIONS: PAEs increase the risk of subfecundity in preconception couples. Our research reinforced the need of monitoring PAE exposure for the purpose of improving human reproductive health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".