The association between ethnicity and pre‐eclampsia in Australia: A multicentre retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Rates of pre-eclampsia vary between countries and certain ethnic groups. However, there is limited evidence about the impact of ethnicity on risk of pre-eclampsia, beyond established clinical risk factors. AIMS: To assess the association between ethnicity and pre-eclampsia in Australia's diverse multi-ethnic population. MATERIALS AND METHODS: We conducted a retrospective cohort study using the ObstetriX database. We included all women with a birth between January 2011 and December 2014, at Auburn, Blacktown/Mount-Druitt and Westmead Hospitals in the Western Sydney Local Health District. We estimated the pre-eclampsia rate overall, and by maternal ethnic group, defined by country of birth and primary language. We developed multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CIs) for pre-eclampsia, adjusting for maternal age, body mass index, autoimmune disease, chronic hypertension, chronic renal disease, diabetes mellitus (type 1 or 2), and multiple pregnancy. A secondary analysis was restricted to nulliparous women. RESULTS: There were 40 824 women evaluated, including 12 743 nulliparous women. Of these, 1448 (3.5%) developed pre-eclampsia (range: Australian/New Zealand-born English speakers 735/15 422 (4.8%); North-East Asian women 51/4470 (1.1%)). Relative to Australian/New Zealand-born English speakers, immigrants had a lower risk of pre-eclampsia overall (adjusted OR 0.67; 95% CI 0.60-0.75); as did the three largest immigrant groups examined: Southern Asian (0.73; 0.62-0.85), Middle-Eastern/African (0.55; 0.47-0.66) and North-East Asian (0.33; 0.25-0.45) women. Findings were similar for nulliparous women. CONCLUSIONS: Certain immigrant groups are at lower risk of pre-eclampsia than Australian/New Zealand-born English-speaking women. Understanding why this is so may lead to better screening and preventive strategies in higher-risk women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".