Variations in very preterm birth rates in 30 high‐income countries: are valid international comparisons possible using routine data?
Bibliographic record
Abstract
OBJECTIVE: Concerns about differences in registration practices across countries have limited the use of routine data for international very preterm birth (VPT) rate comparisons. DESIGN: Population-based study. SETTING: Twenty-seven European countries, the United States, Canada and Japan in 2010. POPULATION: A total of 9 376 252 singleton births. METHOD: We requested aggregated gestational age data on live births, stillbirths and terminations of pregnancy (TOP) before 32 weeks of gestation, and information on registration practices for these births. We compared VPT rates and assessed the impact of births at 22-23 weeks of gestation, and different criteria for inclusion of stillbirths and TOP on country rates and rankings. MAIN OUTCOME MEASURES: Singleton very preterm birth rate, defined as singleton stillbirths and live births before 32 completed weeks of gestation per 1000 total births, excluding TOP if identifiable in the data source. RESULTS: Rates varied from 5.7 to 15.7 per 1000 total births and 4.0 to 11.9 per 1000 live births. Country registration practices were related to percentage of births at 22-23 weeks of gestation (between 1% and 23% of very preterm births) and stillbirths (between 6% and 40% of very preterm births). After excluding births at 22-23 weeks, rate variations remained high and with a few exceptions, country rankings were unchanged. CONCLUSIONS: International comparisons of very preterm birth rates using routine data should exclude births at 22-23 weeks of gestation and terminations of pregnancy. The persistent large rate variations after these exclusions warrant continued surveillance of VPT rates at 24 weeks and over in high-income countries. TWEETABLE ABSTRACT: International comparisons of VPT rates should exclude births at 22-23 weeks of gestation and terminations of pregnancy.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".