Predictors of serum concentrations of polybrominated flame retardants among healthy pregnant women in an urban environment: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Polybrominated diphenyl ethers (PBDEs) are a class of brominated flame retardants commonly used in a wide range of products. Prenatal exposure to PBDEs has been associated with adverse neurodevelopment. Our objective was to characterize predictors of exposure to PBDEs among a multi-ethnic, low-income cohort of pregnant women enrolled from highly urban communities in New York City between years 2009-2010. METHODS: During the first half of pregnancy we collected 316 maternal serum samples and administered an extensive questionnaire including items on demographics, diet and lifestyle. We measured 12 PBDE congeners in blood samples. Using bivariate and multivariate approaches, we regressed the most commonly detected PBDE congeners (PBDE-47, -99, -100 and -153) against potential demographic, dietary and lifestyle predictor variables. RESULTS: At least one PBDE congener was detected in each serum sample. Our analyses demonstrate unique predictor patterns for PBDE-47, -99, -100 and -153 based on demographic, lifestyle and dietary characteristics of women. Higher education and increased use of household electronics were associated with higher levels of all 4 congeners. Six characteristics were associated with PBDE-153 serum concentrations, more than for any other congener. These include maternal education, household income, body mass index, solid dairy consumption, processed meat consumption and frequent use of household electronics. CONCLUSIONS: PBDE exposure in this widespread in this cohort, though levels are lower than previous assessments of US pregnant women. Lower levels may be in response to legislation restricting the production, sale and use of these compounds. In our cohort, we did not observe any individual predictor or a consistent pattern of several predictors representing a significant source of PBDE exposure. These data suggest that legislation and policy may be more effective at reducing exposure than personal lifestyle modifications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".