Pregnancy outcome and ultraviolet radiation; A systematic review
Bibliographic record
Abstract
BACKGROUND: Season and vitamin D are indirect and direct correlates of ultraviolet (UV) radiation and are associated with pregnancy outcomes. Further to producing vitamin D, UV has positive effects on cardiovascular and immune health that may support a role for UV directly benefitting pregnancy. OBJECTIVES: To investigate the effects of UV exposure on pregnancy; specifically fetal growth, preterm birth and hypertensive complications. METHODS: We conducted a systematic review of Medline, EMBASE, DoPHER, Global Health, ProQuest Public Health, AustHealth Informit, SCOPUS and Google Scholar to identify 537 citations, 8 of which are included in this review. This review was registered on PROSPERO and a. narrative synthesis is presented following PRISMA guidance. RESULTS: All studies were observational and assessed at high risk of bias. Higher first trimester UV was associated with and improved fetal growth and increased hypertension in pregnancy. Interpretation is limited by study design and quality. Meta-analysis was precluded by the variety of outcomes and methods. DISCUSSION: The low number of studies and risk of bias limit the validity of any conclusions. Environmental health methodological issues are discussed with consideration given to design and analytical improvements to further address this reproductive environmental health question. CONCLUSIONS: The evidence for UV having benefits for pregnancy hypertension and fetal growth is limited by the methodological approaches utilized. Future epidemiological efforts should focus on improving the methods of modeling and linking widely available environmental data to reproductive health outcomes.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".