Maternal Risk Factors for Preterm Birth in Murmansk County, Russia: A Registry‐Based Study
Bibliographic record
Abstract
BACKGROUND: Globally, about 11% of all liveborn infants are preterm. To date, data on prevalence and risk factors of preterm birth (PTB) in Russia are limited. The aims of this study were to estimate the prevalence of PTB in Murmansk County, Northwestern Russia and to investigate associations between PTB and selected maternal factors using the Murmansk County Birth Registry. METHODS: We conducted a registry-based study of 52 806 births (2006-2011). In total, 51 156 births were included in the prevalence analysis, of which 3546 were PTBs. Odds ratios with 95% confidence intervals of moderate-to-late PTB, very PTB and extremely PTB for a range of maternal characteristics were estimated using multinomial logistic regression, adjusting for potential confounders. RESULTS: The overall prevalence of PTB in Murmansk County was 6.9%. Unmarried status, prior PTBs, spontaneous and induced abortions were strongly associated with PTB at any gestational age. Maternal low educational level increased the risk of extremely and moderate-to-late PTB. Young (<18 years) or older (≥35 years) mothers, graduates of vocational schools, underweight, overweight/obese mothers, and smokers were at higher risk of moderate-to-late PTB. Secondary education, alcohol abuse, diabetes mellitus, or gestational diabetes were strongly associated with moderate-to-late and very PTB. CONCLUSIONS: The observed prevalence of PTB (6.9%) in Murmansk County, Russia was comparable with data on live PTB from European countries. Adverse prior pregnancy outcomes, maternal low educational level, unmarried status, alcohol abuse, and diabetes mellitus or gestational diabetes were the most common risk factors for PTB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".