The Kola Birth Registry and perinatal mortality in Mončegorsk, Russia
Bibliographic record
Abstract
BACKGROUND: A population-based birth registry has been set up for the Arctic town of Moncegorsk in north-western Russia. In this investigation, the quality and the content of the registry are assessed and the perinatal mortality (PM) rates in the period 1973-97 estimated. MATERIAL AND METHOD: Enrollment in the Kola Birth Registry (KBR) involved the retrospective inclusion of all births with at least 28 weeks of gestation in Moncegorsk in the period 1973-97. The data in the registry were assessed for data entry errors, completeness of data and population coverage. The annual PM rates were estimated for live- and stillborns with at least 28 weeks of gestation. RESULTS: The KBR contains detailed information about the newborn, delivery, pregnancy and mother for 21 214 births by women from Moncegorsk, covering at least 96% of all the births by the population in the period studied. No records were missing data for gender and birth date of the newborn, and more than 99.9% of the records contained data about gestational age and birthweight. Data concerning the mothers' employment were missing in 0.4% of the records. The annual PM rate fell from more than 20 to less than 10 deaths per 1000 births during this period. CONCLUSION: The KBR provides an extensive data source useful for case-control and register-based prospective studies, and constitutes the first such compilation in Russia. The homogeneity of the population in Moncegorsk makes it advantageous for epidemiological investigations. The PM rate in Moncegorsk was lower than the overall rate in Russia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".