Preterm preeclampsia in relation to country of birth
Bibliographic record
Abstract
OBJECTIVE: To examine the association between maternal country of birth and the risk of preeclampsia+preterm birth (PTB). STUDY DESIGN: We completed a population-based study in the entire province of Ontario, where there is universal access to obstetrical care. We included 881 700 singleton livebirths among Canadian-born mothers and 305 547 births among immigrant mothers. Adjusted risk ratios (aRRs) were adjusted for maternal age, parity and income quintile. RESULTS: Compared with a rate of preeclampsia+PTB of 4.0 per 1000 among Canadian-born mothers, the aRR of preeclampsia+PTB at 24 to 36 weeks was significantly higher for immigrant women from Nigeria (1.79, 95% confidence interval (CI) 1.12 to 2.84), the Philippines (1.54, 95% CI 1.30 to 1.86), Colombia (1.68, 95% CI 1.04 to 2.73), Jamaica (2.06, 95% CI 1.66 to 2.57) and Ghana (2.12, 95% CI 1.40 to 3.21). The aRRs generally followed a similar pattern for secondary outcomes. Specifically, women from Ghana were at highest risk of preeclampsia+very PTB (4.55, 95% CI 2.57 to 8.06), and women from Jamaica at the highest risk of preeclampsia+indicated PTB (1.89, 95% CI 1.43 to 2.50). CONCLUSION: The risk of preeclampsia+PTB is highest among women from a select number of countries. This information can enhance initiatives aimed at reducing the risk of PTB related to preeclampsia.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".