Acute temporal effect of ambient air pollution on common congenital cardiovascular defects and cleft palate: a case-crossover study
Bibliographic record
Abstract
Abstract This symmetric bidirectional case-crossover study examined the association between short-term ambient air pollution exposure during weeks 3-8 of pregnancy and certain common congenital anomalies in Ulaanbaatar, Mongolia, between 2014 and 2018. Using predictions from a Random Forest regression model, authors assigned daily ambient air pollution exposure of particulate matter <2.5 um aerodynamic diameter, sulphur dioxide, nitrogen dioxide, and carbon monoxide for each subject based on their administrative area of residence. We used conditional logistic regression with adjustment for corresponding apparent temperature to estimate relative odds of select congenital anomalies per IQR increase in mean concentrations and quartiles of air pollutants. The adjusted relative odds of cardiovascular defects (ICD-10 subchapter: Q20-Q28) was 2.64 (95% confidence interval: 1.02-6.87) per interquartile range increase in mean concentrations of particulate matter <2.5 um aerodynamic diameter for gestational week 7. This association was further strengthened for cardiac septal defects (ICD-10 code: Q21, odds ratio: 7.28, 95% confidence interval: 1.6-33.09) and isolated ventricular septal defects (ICD-10 code: Q21.0, odds ratio: 9.87, 95% confidence interval: 1.6-60.93). We also observed an increasing dose-response trend when comparing the lowest quartile of air pollution exposure with higher quartiles on weeks 6 and 7 for Q20-Q28 and Q21 and week 4 for Q21.0. Other notable associations include increased relative odds of cleft lip and cleft palate subchapter (Q35-Q37) and PM 2.5 (OR: 2.25, 95% CI: 0.62-8.1), SO 2 (OR: 2.6, 95% CI: 0.61-11.12), and CO (OR: 2.83, 95% CI: 0.92-8.72) in week 4. Our findings contribute to the limited body of evidence regarding the acute effect of ambient air pollution exposure on common adverse birth outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".