Perinatal mortality in relation to birthweight and gestational age: a registry‐based comparison of Northern Norway and Murmansk County, Russia
Bibliographic record
Abstract
The objective was to explore how perinatal mortality relates to birthweight, gestational age and optimal perinatal survival weight for two Arctic populations employing an existing and a newly established birth registry. A medical birth registry for all births in Murmansk County of North-West Russia became operational on 1st January 2006. Its primary function is to provide useful information for health care officials pertinent to improving perinatal care. The cohort studied consisted of 17,302 births in 2006-07 (Murmansk County) and 16,006 in 2004-06 (Northern Norway). Birthweight probability density functions were analysed, and logistic regression models were employed to calculate gestational-age-specific mortality ratios. The perinatal mortality rate was 10.7/1000 in Murmansk County and 5.7/1000 in Northern Norway. Murmansk County had a higher proportion of preterm deliveries (8.7%) compared to Northern Norway (6.6%). The odds ratio (OR) of risk of mortality (Northern Norway as the reference group) was higher for all gestational ages in Murmansk County, but the largest risk difference occurred among term deliveries (OR 2.45, 95% confidence interval 1.45, 4.14) which hardly changed on adjustment for maternal age, parity and gestation. Proportionately, more babies were born near (± 500 g) the optimal perinatal survival weight in Murmansk County (67.2%) than in Northern Norway (47.6%). The observed perinatal mortality was higher in Murmansk County at all birthweight strata and at gestational ages between weeks 25 and 42, but the adjusted risk difference was most significant for term deliveries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".