Exposure to traffic noise and gestational weight gain and postpartum weight retention: a cohort study
Bibliographic record
Abstract
OBJECTIVES: Transportation noise has been associated with markers of obesity. We aimed to investigate whether road traffic and railway noise were associated with weight gain during and after pregnancy. METHODS: Among the women participating in the Danish National Birth Cohort, 74 065 reported on weight before and during the pregnancy (gestational week 30) and 52 661 reported on weight before and 18 months after pregnancy. Residential address history from conception to 18 months after pregnancy was obtained in national registers, and road traffic and railway noise were modelled for all addresses. Associations between noise and gestational weight gain (GWG) and postpartum weight retention (PPWR) were analysed using the linear and log-binomial regression. RESULTS: A 10 dB(A) higher road traffic noise was associated with an increase in GWG of 3.8 g/week (95% CI 2.3 to 5.3) and PPWR of 0.09 kg (95% CI 0.02 to 0.16). For PPWR, this association seemed confined to women who were overweight (0.17 kg, 95% CI 0.02 to 0.32) or obese (0.49 kg, 95% CI 0.26 to 0.73) before pregnancy. Further adjustment by nitrogen dioxide reduced GWG risk estimates and slightly increased PPWR risk estimates. Railway noise ≥65 dB(A) was associated with an increase in GWG of 4.5 g/week (95% CI -2.7 to 11.6) and PPWR of 0.26 kg (95% CI -0.09 to 0.60) compared with levels <55 dB(A). CONCLUSIONS: Our findings suggest that road traffic noise is associated with weight gain during and after the pregnancy, which adds to the literature linking transportation noise to adiposity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".