A Randomized Controlled Trial of HEPA Filter Air Cleaner Use and Fetal Growth in Ulaanbaatar, Mongolia
Bibliographic record
Abstract
Introduction: Fine particulate matter (PM2.5) exposures may impair fetal growth. We conducted a randomized controlled trial to assess the efficacy of portable HEPA filter air cleaners to reduce fetal growth restriction among non-smoking pregnant women in Ulaanbaatar, Mongolia, where residential coal use leads to high ambient PM2.5 concentrations. Methods: We randomly assigned 540 women to an intervention (1-2 air cleaners) or control (no air cleaners) group. One-week continuous PM2.5 measurements were made at approximately 12 and 30 weeks gestation. Housing, maternal health, and birth outcome (weight, length, head circumference, and gestational age) data were obtained via questionnaires and clinical records. We used linear regression to examine the efficacy of the intervention on birth outcomes for all live births (n=463) and term births (n=424), adjusting for gestational age, sex and parity, in an intention-to-treat analysis. Results: Based on 690 PM2.5 measurements, the geometric mean (GSD) concentration was 26 % lower in intervention homes [35 (2) vs. 47 (2) µg/m3]. The mean (SD) birth weight, birth length, and head circumference were 3457 (545) g, 51 (3) cm, and 35 (2) cm, respectively, among all live births and there were no differences by group assignment. In adjusted models of all births, the intervention did not result in a significant difference in birth weight (49 g; 95% CI: -27, 125 g; p=0.20), birth length (0.2 cm; 95% CI: -0.2. 0.6 cm; p=0.24), or head circumference (0.1 cm; 95% CI: -0.2, 0.4 cm; p=0.67). Among term births, the intervention resulted in a marginally significant increase in birth weight [68 g (95% CI: -5, 142 g; p=0.07] and a 0.4 cm increase in birth length (95% CI: 0.04, 0.7 cm; p=0.03). Conclusion: HEPA filter air cleaner use during pregnancy in this high pollution setting is associated with improved fetal growth as indicated by greater birth length and birth weight among term births.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".