Bibliographic record
Abstract
OBJECTIVE: To determine whether the preterm birth rate was elevated in two urban areas of Ukraine, a former eastern bloc country that experienced serious economic, social, and health problems during its transition from a socialist republic. METHODS: We identified every pregnancy in a defined period in two urban sites where a separate study of pregnancy and childhood was being conducted. We obtained gestational age and vital status at delivery for each. Information about onset of labor and conduct of delivery was available for the subgroup enrolled in the collaborating study. RESULTS: Among 17,137 pregnancies, all but 6774 were terminated voluntarily. Among the continuing pregnancies, the preterm birth rate was 6.6% for live-born singletons of 20 or more weeks' gestation. Only 12% of preterm births involved medical intervention, the rest were idiopathic. The preterm birth rate was higher than in Europe (4.0% to 5.4%) and Canada (5.9%) but lower than for whites in the United States (8.4%). CONCLUSION: Live-born preterm birth rates are influenced by whether infants survive to be included in calculations. The high fetal mortality rate in Ukraine causes many preterm births to be excluded, thus lowering the rate. Frequent pregnancy termination and lack of ultrasound dating in Ukraine also might cause the preterm birth rate to be lower. Preterm birth rates, especially among live-born infants, are difficult to interpret and treacherous to compare across nations. Survival of the fetus and its health and development at birth are better indicators of reproductive outcome.
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 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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".