Assessing the multi-dimensional effects of air pollution on maternal complications and birth outcomes: A structural equation modeling approach
Bibliographic record
Abstract
Objective: This cross-sectional study aims to investigate the direct and indirect relationships between exposure to a metal mixture in air and adverse pregnancy outcomes across gestational stages. Methods: and unravel their effects on pregnancy complications (preeclampsia and gestational diabetes) and birth outcomes (low birth weight and preterm birth risks). Results: A latent variable featuring seven metals (Aluminum, Calcium, Iron, Magnesium, Manganese, Silicon, Vanadium) was identified through the measurement model. The latent metal mixture exposure had direct effects on gestational diabetes and preterm birth (1st trimester, 2nd trimester), low birth weight (1st trimester), and preeclampsia (2nd trimester). When considering total effects, the effects on low birth weight in the 1st trimester and on preeclampsia in 2nd trimester were masked, and the latent metal mixture increased the low-birth-weight risk in 2nd trimester by 2 % (OR = 1.02, 95 % CI = [1.00, 1.03]). Conclusion: , and provides additional evidence for understanding the pathway of the pollution effects on fetal health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".