Night work and miscarriage: a Danish nationwide register-based cohort study
Bibliographic record
Abstract
OBJECTIVE: Observational studies indicate an association between working nights and miscarriage, but inaccurate exposure assessment precludes causal inference. Using payroll data with exact and prospective measurement of night work, the objective was to investigate whether working night shifts during pregnancy increases the risk of miscarriage. METHODS: A cohort of 22 744 pregnant women was identified by linking the Danish Working Hour Database (DWHD), which holds payroll data on all Danish public hospital employees, with Danish national registers on births and admissions to hospitals (miscarriage). The risk of miscarriage during pregnancy weeks 4-22 according to measures of night work was analysed using Cox regression with time-varying exposure adjusted for a fixed set of potential confounders. RESULTS: In total 377 896 pregnancy weeks (average 19.7) were available for follow-up. Women who had two or more night shifts the previous week had an increased risk of miscarriage after pregnancy week 8 (HR 1.32 (95% CI 1.07 to 1.62) compared with women, who did not work night shifts. The cumulated number of night shifts during pregnancy weeks 3-21 increased the risk of miscarriages in a dose-dependent pattern. CONCLUSIONS: The study corroborates earlier findings that night work during pregnancy may confer an increased risk of miscarriage and indicates a lowest observed threshold level of two night shifts per week.
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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 | 0.002 |
| 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".