Short-term Impact of Ambient Air Pollution and Air Temperature on Blood Pressure Among Pregnant Women
Bibliographic record
Abstract
BACKGROUND: Epidemiologic studies have reported inconsistent findings for the association between air pollution levels and blood pressure (BP), which has been studied mainly in elderly subjects. Short-term air pollution effects on BP have not been investigated in pregnant women, who may constitute a vulnerable population. METHODS: Between 2002 and 2006, 1500 pregnant women from a mother-child cohort study conducted in Nancy and Poitiers, France, underwent 11,220 repeated BP measurements (average, 7.5 measurements/woman). Nitrogen dioxide (NO₂), particulate matter with an aerodynamic diameter below 10 μm (PM₁₀), and meteorologic variables were measured on an hourly basis at permanent monitoring sites. We studied changes of BP in relation to short-term variations of air pollution and temperature with mixed models adjusted for meteorologic and personal characteristics. RESULTS: A 10°C decrease in temperature led to an increase in systolic BP of 0.5% (95% confidence interval = 0.1% to 1.0%). Elevated NO₂-levels 1 day, 5 days and averaged over 7 days before the BP measurement were associated with reduced systolic BP. The strongest decrease was observed for the 7-day NO₂ average (-0.4% [-0.7% to -0.2%] change for an 11 μg/m³ increase in NO₂). PM₁₀ effects on systolic BP differed according to pregnancy trimester: PM₁₀ concentration was associated with systolic BP increases during the first trimester and systolic BP decreases later in pregnancy. CONCLUSIONS: We observed short-term associations of air pollution and of temperature with BP in pregnant women. Whether such changes in BP have clinical implications remains to be investigated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".