Effects of acute ambient pollution exposure on preterm prelabor rupture of membranes: A time-series analysis in Shanghai, China
Bibliographic record
Abstract
While the effects of ambient pollutants on adverse perinatal outcomes have been studied, most studies have focused on preterm birth, stillbirth, and low birthweight. Few studies have examined the effects of ambient pollutants on prelabor rupture of membranes (PROM). This study was designed to explore the acute effects of ambient pollutants on both term PROM (TPROM) and preterm PROM (PPROM). We enrolled pregnant women receiving antenatal care between October 2013 and December 2019 at the International Peace Maternity and Child Health Hospital (IPMCHH). The effects of ambient pollutants (including PM2.5, PM10, SO2, CO, NO2, and 8-h O3) on TPROM and PPROM were estimated using generalized additive models (GAMs). Exposure-response relationship curves were also evaluated using GAMs after adjustment for confounding factors. Potential lagged effects were examined using various lag models. The data of 100,200 pregnant women who delivered at IPMCHH were analyzed. The fitted spline curves for PPROM were similar to the temporal trends of PM2.5, PM10, SO2, CO and NO2 but not O3, while those for TPROM were different from the temporal trends of all six air pollutants. An increased risk of PPROM was associated with increased concentrations of PM2.5, PM10, SO2 and CO on lag days 2 and 3, while no association was found between PPROM and daily concentration of O3. After adjustment for confounding factors, there was a shift in the exposure-response curves, indicating associations between PPROM and PM2.5, PM10, SO2, and CO on lag days 2–3. Interaction effects of PM2.5, PM10, SO2, and CO were also found to increase the risk of PPROM. In conclusion, acute exposures to six critical air pollutants were not associated with an increased risk of TPROM; however, PM2.5, PM10, SO2, and CO were found to interact, increasing the risk for PPROM on lag days 2 and 3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".