Predicting Prenatal Exposure to Polybrominated Diphenyl Ethers (Pbdes), p,p-Dichlorodiphenyltrichloroethane (p,p-DDT), and p,p-Dichlorodiphenyldichloroethylene (p,p-DDE) from Maternal and Child Blood Levels 9 Years after Delivery
Bibliographic record
Abstract
Predicting Prenatal Exposure to Polybrominated Diphenyl Ethers (Pbdes), p,p'-Dichlorodiphenyltrichloroethane (p,p'-DDT), and p,p'-Dichlorodiphenyldichloroethylene (p,p'-DDE) from Maternal and Child Blood Levels 9 Years after DeliveryAbstract Number:2248 Fraser Gaspar*, Marc-Andre Verner, Jonathan Chevrier, Robert Gunier, Andreas Sjodin, Asa Bradman, and Brenda Eskenazi Fraser Gaspar* Center for Environmental Research and Children's Health, United States, E-mail Address: [email protected] , Marc-Andre Verner Channing Division of Network Medicine, Brigham and Women s Hospital, United States, E-mail Address: [email protected] , Jonathan Chevrier McGill University, Canada, E-mail Address: [email protected] , Robert Gunier Center for Environmental Research and Children's Health, United States, E-mail Address: [email protected] , Andreas Sjodin Center for Disease Control and Prevention, United States, E-mail Address: [email protected] , Asa Bradman Center for Environmental Research and Children's Health, United States, E-mail Address: [email protected] , and Brenda Eskenazi Center for Environmental Research and Children's Health, United States, E-mail Address: [email protected] AbstractPrenatal exposure to polybrominated diphenyl ethers (PBDEs), p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT), and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) have been associated with adverse health outcomes in children. In studies where blood levels of these chemicals are only available in women or children after birth, researchers would benefit from tools to estimate prenatal exposure levels. We evaluated a life-course pharmacokinetic model and predictive models using deletion/substitution/addition or SuperLearner algorithms to predict prenatal exposure to PBDEs (BDE-47, -99, -100 and -153) and p,p'-DDT/E from maternal and/or child blood levels measured 9-years after delivery. The three approaches were compared using the root mean squared error (RMSE) and coefficient of determination (R2). For all compounds, SuperLearner outperformed the other approaches with RMSEs and R2s ranging from 0.10-0.20 ng/g and 0.55-0.97, respectively. Typically, model RMSEs were lower and R2s were higher for p,p'-DDT/E than PBDE congeners, and prediction using maternal levels 9-years after delivery (n=94) were more precise compared to child levels 9-years after delivery (n=161). The pharmacokinetic model performed well when predicting compounds with longer half-lives such as p,p'-DDT/E and BDE-153 (RMSEs and R2s= 0.17-0.28 ng/g and 0.57-0.88, respectively).Results demonstrate the ability to accurately predict prenatal levels from maternal POP blood levels 9 years after delivery, with SuperLearner performing the best based on our fit criteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".