Urinary Concentrations of Phthalate Metabolites and Pregnancy Loss Among Women Conceiving with Medically Assisted Reproduction
Bibliographic record
Abstract
BACKGROUND: Animal studies demonstrate that several phthalates are embryofetotoxic and are associated with increased pregnancy loss and malformations. Results from human studies on phthalates and pregnancy loss are inconsistent. METHODS: We examined pregnancy loss prospectively in relation to urinary phthalate metabolite concentrations among women undergoing medically assisted reproduction. We used data from 256 women conceiving 303 pregnancies recruited between 2004 and 2012 from the Massachusetts General Hospital Fertility Center. We quantified 11 phthalate metabolite concentrations and calculated the molar sum of four di(2-ethylhexyl) phthalate (DEHP) metabolites (ΣDEHP). We estimated risk ratios (RRs) and 95% confidence intervals for biochemical loss and total pregnancy loss (<20 weeks' gestation) across quartiles using repeated measures log-binomial models, adjusted for age, body mass index, smoking and infertility diagnosis. RESULTS: Of the 303 pregnancies, 83 (27%) ended in loss less than 20 weeks' gestation and among these, 31 (10%) ended in biochemical loss. Although imprecise, the RRs for biochemical loss increased across quartiles of ΣDEHP and three individual DEHP metabolites. For ΣDEHP, the RRs (confidence intervals) were 2.3 (0.63, 8.5), 2.0 (0.58, 7.2), and 3.4 (0.97, 11.7) for quartiles two, three, and four, compared with one, respectively (P trend = 0.04). RRs for total pregnancy loss were elevated in the highest quartiles of ΣDEHP and three DEHP metabolites. The remaining seven phthalate metabolite concentrations evaluated were not associated with either outcome. CONCLUSIONS: We found a suggestive pattern of association between conception cycle-specific urinary concentrations of DEHP metabolites and biochemical and total pregnancy loss among women undergoing medically assisted 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".