The data we have: Pregnancy and birth related data collection in Australia, Canada, Europe and the USA- A web-based survey of practice
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility of combining routinely recorded perinatal data from several databases in high-income countries to assess the risk of recurrent stillbirth. METHODS: Web-based questionnaire survey with reminder emails and searching of relevant country websites. RESULTS: 120 countries/regions in Canada, Europe and the USA were invited to participate and 83 (69%) responded. Of those one had no data, and two did not wish to take part. The remaining 80 were sent the questionnaire and 63 (53%) were completed. Twenty-seven countries/regions reported that they collect information on all perinatal events (including early pregnancy loss), 34 on live births and stillbirths and two only live births (stillbirths recorded in a separate database). Most countries (53/63) can link two or more pregnancies occurring in the same woman. Data and information extracted from the Australian and New Zealand Government websites showed that information on all perinatal events is collected nationally in New Zealand and in 5/8 regions in Australia. Both Australia and New Zealand can link two or more pregnancies occurring in the same woman. Maternal age and caffeine consumption were the most and least consistently collected demographic indicators respectively. Diabetes mellitus and mental health problems, birthweight and obstetric cholestasis the most and least consistently collected for medical conditions and pregnancy condition/complications. Procedures for gaining access to data vary between countries. CONCLUSION: This study demonstrates that it is possible to link pregnancies in the same woman to assess the risk of recurrent stillbirth using routinely collected perinatal data in all states/territories in Australia, 7/8 responding provinces/territories in Canada, 21/27 responding countries/regions in Europe, New Zealand and 26/28 responding states in the USA. The scope of the databases and quality and extent of data collected (thus their potential use) varied, as did procedures for accessing their data.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".