Night shift work surrounding pregnancy and offspring risk of atopic disease
Bibliographic record
Abstract
BACKGROUND: Night shift work surrounding pregnancy may contribute to the risk of developing atopic diseases in offspring due to alterations in the prenatal environment, from stress. OBJECTIVE: To examine the association of maternal night shift work surrounding pregnancy and offspring risk of developing atopic diseases from childhood to adolescence. METHODS: We examined the association between night shift work before and during pregnancy among 4,044 mothers in the Nurses' Health Study II (NHSII) and atopic dermatitis, asthma and hay fever risk in 4,813 of their offspring enrolled in the Growing Up Today Study (GUTS). Mothers reported whether GUTS participants had ever been diagnosed with atopic dermatitis, asthma or hay fever in the GUTS Mothers' questionnaire. Generalized estimating equation regression models were used to estimate multivariable adjusted odds ratios (OR) and 95% confidence intervals (CIs). RESULTS: There were no significant associations between pre-conception maternal night shift work and risk of atopic dermatitis, asthma or hay fever in their offspring. Among 545 mothers with information on night shift work during pregnancy, shift work also was not associated with atopic dermatitis, asthma or hay fever in the offspring. Stratified analyses by history of parental atopy and maternal chronotype showed some statistically significant findings, but they were inconsistent and no significant interaction was seen with increasing duration of night shift work. CONCLUSION: In this study, night shift work before and during pregnancy did not increase offspring risk of developing atopic dermatitis, asthma or hay fever.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".