Prenatal Exposure to Air Pollution and Respiratory Distress in Term Newborns: Results from the MIREC Prospective Pregnancy Cohort
Bibliographic record
Abstract
BACKGROUND: Respiratory distress is the leading cause of neonatal morbidity and mortality worldwide, and prenatal exposure to air pollution is associated with adverse long-term respiratory outcomes; however, the impact of prenatal air pollution exposure on neonatal respiratory distress has not been well studied. OBJECTIVES: ) with respiratory distress and related neonatal outcomes. METHODS: ) were estimated using land-use regression and satellite-derived models coupled with ground-level monitoring and linked to participants based on residential location at birth. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for associations between air pollution and physician-diagnosed respiratory distress in term neonates in hierarchical logistic regression models adjusting for detailed maternal and infant covariates. RESULTS: ; 95% CI: 1.00, 1.06). DISCUSSION: increased the risk of severe respiratory distress among term newborns. These findings support the development and prioritization of public health and prenatal care strategies to increase awareness and minimize prenatal exposures to air pollution. https://doi.org/10.1289/EHP12880.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".