Association of ischemic placental disease in a Southern California Birth Cohort and PM<sub>2.5</sub> chemical species and oxidative potential markers
Bibliographic record
Abstract
Abstract Road traffic is a significant source of particulate matter pollution, whose exposure is a significant risk factor in pregnancy-related health outcomes. The exact mechanisms behind the relationship between traffic-related air pollution (TRAP) exposure and adverse pregnancy outcomes remain unclear. We aim to assess the relationship between exposure to brake and tire wear-associated metals and oxidative potential and ischemic placental disease (IPD). Data were assembled from a final population of 178 women who sought specialized prenatal care at UCLA between 2016 and 2019 in Los Angeles, CA. Modeled first trimester exposures to chemical constituents and oxidative stress potential of PM 2.5 , black carbon, and PM 2.5 mass concentration. Speciated measurements included tracers of brake wear (barium), tire wear (zinc), and oxidative potential markers based on metal concentrations (KM-SUB-ELF ROS) or laboratory assays (DTT loss, OH radical formation). Exposures were modeled by integrating data from filter samples, a low-cost PM 2.5 sensor network, and land-use data. We used logistic regression to estimate the associations between air pollution exposures and IPD, adjusting for covariates assessed through medical records and interviews. Scaled to the interquartile range, odds ratios (95% CI) were as follows: barium OR: 1.7 (1.1, 2.7), zinc OR: 1.4 (.86, 2.4), and oxidative potential markers, both modeled as well as measured through DTT loss and OH formation assays (ORs ranging from 1.1-2.0). Point estimates of effect sizes for PM 2.5 and black carbon were lower than most measurements (ORs: 1.3-1.4). mass and black carbon. Our findings suggest two key points: (i) metals associated with brake and tire wear, currently unregulated, may play a role in the relationship between TRAP and adverse pregnancy outcomes, and (ii) reducing tailpipe emissions may not be sufficient to protect pregnant women from TRAP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".