Bibliographic record
Abstract
In epidemiological studies of environmental exposures and adverse pregnancy outcomes, maternal residence at delivery is often used to assign an exposure level, based on routinely collected data. In order to examine the potential for exposure misclassification due to residential mobility, we examined maternal mobility according to changes in residence overall, as well as changes in municipality and county. The potential for mobility to be related to pregnancy outcomes was considered by examining the relationship between mobility and risk factors commonly included in investigations of adverse pregnancy outcomes. Previously collected data were studied from 398 population-based control subjects from a case-control study of stillbirths. We compared demographic, lifestyle, medical, pregnancy and environmental factors of women who moved during pregnancy with those who did not. Bivariable and multivariable log binomial regressions were used to identify risk factors that were associated with mobility during pregnancy. Twelve per cent of subjects moved at least once during their pregnancy. Among women who moved, the majority (62%) moved within the same municipality. In bivariable analyses, we found that low family income, lower maternal age, unmarried status and tobacco use were associated with an increased likelihood of moving during pregnancy, whereas women who used folic acid before conception and who had a higher prepregnancy body mass index (BMI) were less likely to move during pregnancy. In multivariable analyses, only family income, age and prepregnancy BMI were independently predictive of mobility. These results indicate that in studies using maternal residence at delivery to assign environmental exposures, mobility during pregnancy is likely to be prevalent enough to introduce exposure misclassification. The potential for differential exposure misclassification should be considered should any of the risk factors for moving identified by this study also be risk factors for the outcome under study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".