Season of Conception, Smoking, and Preeclampsia in Norway
Bibliographic record
Abstract
BACKGROUND: Preeclampsia (PE) is a dangerous and unpredictable pregnancy complication. A seasonal pattern of risk would suggest that there are potentially preventable environmental contributors, but prior analyses have not adjusted for confounding by PE risk factors that are associated with season of conception. METHODS: Seasonal effects were modeled and tested by representing each day of the year as an angle on a unit circle and using trigonometric functions of those angles in predictive models, using "harmonic analysis." We applied harmonic Cox regression to model confounder-adjusted effects of the estimated day of the year of conception on risk of PE for births from the Medical Birth Registry of Norway for deliveries between 1999 and 2009. We also examined effect measure modification by parity, latitude (region), fetal sex, and smoking. RESULTS: In adjusted models, PE risk was related to season, with higher risk in spring conceptions and lower risk in autumn conceptions, with a risk amplitude (maximum compared with minimum) of about 20%. The pattern replicated across subpopulations defined by parity, latitude (region), fetal sex, and smoking. CONCLUSIONS: These results suggest that there is a seasonal driver for PE, with effects that are not modified by parity, latitude, fetal sex, or smoking. https://doi.org/10.1289/EHP963.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".