Night work during pregnancy and small for gestational age: a Danish nationwide register-based cohort study
Bibliographic record
Abstract
OBJECTIVE: The aim was to investigate the association between night work during pregnancy and risk of having a small for gestational age (SGA) child. METHODS: This cohort study had payroll data with detailed information on working hours for employees in all Danish administrative regions (primarily hospital employees) between 2007 and 2015, retrieved from the Danish Working Hour Database. Pregnancies, covariates and outcome were identified from the national birth registry. We used logistic regression to investigate the association between intensity and duration of night work during the first 32 pregnancy weeks and SGA. The adjusted model included age, body mass index, socioeconomic status and smoking. Using quantitative bias analysis and G-estimation, we explored potential healthy worker survivor bias (HWSB). RESULTS: The final cohort comprised 24 548 singleton pregnancies in 19 107 women, primarily nurses and medical doctors. None of the dimensions of night work were associated with an increased risk of SGA. We found a tendency towards higher risk of SGA in pregnancies where the women stopped having night shifts during pregnancy. Using G-estimation we found an OR<1 for the association between night work and SGA if all workers continued having night work during pregnancy compared with daywork only. CONCLUSION: We found no increased risk of SGA in association with night work during pregnancy among healthcare workers. G-estimation was not precise enough to estimate the observed indication of HWSB. We need better data on pregnancy discomforts and complications to be able to safely rule out HWSB.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".