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Record W2756341386 · doi:10.1289/ehp963

Season of Conception, Smoking, and Preeclampsia in Norway

2017· article· en· W2756341386 on OpenAlexaff
Clarice R. Weinberg, Min Shi, Olga Basso, Lisa A. DeRoo, Quaker E. Harmon, Allen J. Wilcox, Rolv Skjærven

Bibliographic record

VenueEnvironmental Health Perspectives · 2017
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsMcGill University
FundersNorwegian Institute of Public HealthNational Institutes of HealthNational Institute of Environmental Health SciencesNorges Forskningsråd
KeywordsConfoundingDemographyPregnancyMedicinePreeclampsiaSeason of birthParity (physics)Relative riskSeasonalityLatitudeObstetricsMathematicsStatisticsConfidence intervalGeographyInternal medicineBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.314
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

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